Introduction

“I was neither able to sleep, nor was I able to move out. Many don’t take these medications because of this fear only. ” This was from an unnamed 40-year-old rural male patient from Nagpur, India, who reported adverse drug effects as a barrier for treatment adherence.

“I felt like I was going up and down; I could not sleep the whole night. Taking 12-13 pills was impossible for me… I am already weak, even when you utter my name of taking medicine, my head starts cracking. ”

This was from another rural male patient, but this time 28 years of age, who also mentioned that the side effects were exacerbated due to the quantity of pills and consistency of time required to complete treatment, another key factor as to why long treatment fails.

The above quotes represent rural patients’ experiences with multidrug-resistant tuberculosis and its health effects (Deshmukh, Dhande, et. al, 2015). This came from a study between 2012 and 2013, in the Nagpur Drug Resistant TB Centre, a drug resistant tuberculosis center in India, where patients were randomly chosen to describe their feelings after an intensive drug prescription session after they had been diagnosed with multidrug-resistant tuberculosis, one of the most dangerous infectious diseases in India right now, if not the most dangerous, according to the CDC as of 2024. Patients have had difficulty adhering to treatments and consistently upholding their regimens due to many reasons; it could all be too much for them and draining their energy or they could have a mental stigma against these medications. All of these will be discussed later in the paper. But first, we must learn more about the disease of multidrug-resistant tuberculosis, which is lately causing a lot of problems for patients in India across both urban and rural settings in terms of upheavals of social dynamics and biomedical issues.

Multidrug-resistant tuberculosis (MDR-TB), or rifampicin-resistant tuberculosis (RR-TB) is a major, increasingly dangerous, and virulent infectious disease in today ’ s world. Harboring much of the same symptoms of regular tuberculosis, including fever, chest pain, general weakness, cough, and sputum production, MDR-TB is a more dangerous and form of TB, showing large amounts of resistance to major drug classes and products including rifampicin and isoniazid, both commonly used and powerful first-line drugs to treat TB that are now obsolete to treat MDR-TB. This drug resistance is, from the biomedical perspective, caused by increasing numbers of efflux pumps in MDR-TB cells that pump out antibiotic drugs intended to kill the pathogen and more enzymes that inactivate drugs like rifampicin and isoniazid. As a result, the pathogen becomes more potentially fatal considering there are less options for medical professionals to successfully treat the disease as time goes on.

Discovered in 2012 in a Mumbai hospital, the impacts of MDR-TB have gotten worse for a long period of time, mainly explained by the fact that India continues to have 26% of global TB cases as of 2023 (Mandal, Rao, Joshi, et al.). It has become a public health crisis , as this 26% involves 8.2 million people diagnosed with tuberculosis, 1.23 million of those people dying that year.

However, much of the current medical community overlooks the important sociocultural and socioeconomic factors that play a role in exacerbating the MDR-TB situation in India. In culturally-diverse areas with different ways of living and interpreting the world, disparities are bound to occur in terms of medical treatments and how the government and politicians make relevant policies or participate in corruption with regards to MDR-TB regulation and management. These disparities is a main point of focus for medical anthropologists, who use them to explore the historical, sociocultural, socioeconomic, political, economic, and biomedical discrepancies that set the stage for the current crisis of MDR-TB.

The pathogen’ s history of interventions and attempts at treatments, ranging from physical sanatoria to increasing reliance of pharmaceutical drugs after much biomedical research, paints a picture of how global research, beliefs, and actions taken to address tuberculosis has grown over time, especially considering different perspectives and treatment theories that have sprouted throughout history. In addition, socioeconomic disparities, which tend to be highlighted in a densely-populated developing country like India where even an 11.9% poverty rate (as of 2021) is large due to being the most populated nation (as of April 2023), run rampant, consisting of radical differences and discrimination in opportunities for personal and professional development between urban and rural areas (Forbes India, 2024). As will be discussed later, political pressure and corruption is also there to sometimes curb honest data and initiatives being passed, while pharmaceuticalization has grown to be an integral part of India ’ s GDP and overall economic policy.

Integral to the sociological analysis of the TB crisis is the phenomenon of medicalization, a process in which a certain health problem (whether it has to do with psychological, mental, or cultural illnesses) is transformed into a medical problem, where medication and mainstream medicine picks up treatment of this particular illness. In many cases, medicalization can be of benefit to certain sufferers; utilizing prescription drugs and treatments for psychological conditions like schizophrenia and depression has led to success in treating, controlling, and sometimes curing these illnesses. However, most cases of medicalization in other countries (especially developing countries) have actually caused more harm than benefit, often straying the focus away from the ever-important cultural and sociological impacts and influences of disease (Lantz, Goldberg, Gollust, 2023). Therefore, this paper focuses on the classic examples of medicalization in the context of tuberculosis in India, and how that has inadvertently led to its rising drug resistance.

The late sociologist Peter Conrad found that society is now witnessing the “ shifting engines of medicalization, ” explaining how the agents and factors causing medicalization are now shifting away from medical professionals to entities like the pharmaceutical and biotech industries, propelled by consumer demand and commercial influence. The boom in pharmaceutical drugs and treatments via the multiple microbusinesses and private local health practitioners, providing the bulk of Indian healthcare, add further fuel to current medicalization and drug resistance.

This recent emphasis on medicalization also brings forth another aspect into the issue: sociocultural factors. A culturally rich and diverse nation like India harbors multiple cultural beliefs, customs, and practices relating to the health of their various regional populations. Regional cultures, before the arrival of modern medicine and thought, have tended to view disease, especially tuberculosis, in ways that focused more on the social determinants of health rather than the biomedical ones. As we have seen modern medicine and the current global public health system essentially flip the script on this initial approach, community interactions between the old and new will be integral to developing and understanding holistic approaches towards tackling disease.

Historical

Tuberculosis, let alone MDR-TB, has had a long, complex history. Formally discovered in 1882 by Dr. Robert Koch, tuberculosis had been killing “ one in every seven people in the United States and Europe [at the time], ” according to the CDC. However, TB has existed for thousands of years, even showing up in India through ancient medical records and artifacts. During the early 1900s, India largely used sanatoria (isolated medical facilities focusing on good hygiene and care barring antibiotics) to treat tuberculosis, with varying degrees of success. In 1917, the first TB dispensary–a hub for testing and TB treatment–was opened in Bombay, while the first official national study on TB was conducted in 1914 by Arthur Lankester (Central TB Division, 2025 ).

The introduction of allopathic medicine from Europe to colonized nations like India initiated a focus among doctors in India on the biomedical theories and findings of Dr. Koch. Nevertheless, there were a few dissenters who were more keen in delving into alternative theories about the true causes of tuberculosis.

One of these dissenters was David Chowry-Muthu, a T amil Christian doctor specializing in TB. Apart from setting up the first sanatorium hospital in India in 1928, he is also known for challenging the then-largely-accepted bacterial theory of disease causation to instead emphasize the role of environmental factors like poor living conditions and personal well-being in the reduction of illness while avoiding the excessive inclusion of antibiotics. He outlined this stance in his 1921 book Pulmonary Tuberculosis: Its Etiology and Treatment, also proposing reductions in military expenditures to prevent war-related illnesses and investment in urban planning, economic reforms, and improvement in living standards. Even prominent Indian leaders like Jawaharlal Nehru (the first Prime Minister of India) and Mahatma Gandhi (who spearheaded the Indian independence movement), concurred, discussing environmental factors and familial experiences with TB that supported Chowry-Muthu ’ s theory; Nehru used his experience of his wife ’ s struggle with TB to stress the need for more adequate hospital resources while Gandhi emphasized public health and environmental factors like water and air quality, cleanliness, and sanitation as key players in reducing TB’ s spread.

However, Chowry-Muthu ’ s claims could not gain traction mainly due to the Madras Study done in 1950. This study demonstrated that home-based antibiotic treatment was effective in managing TB. It also initiated a rise in randomized controlled trials (RCTs) as a gold standard to determine treatment efficacy, leading to more critical views of prior treatment methods.

This larger emphasis on patient autonomy in health and medication decisions synthesized the foundation for the current state of the Indian pharmaceutical industry, which controls the means of production, ownership, and transfer of drugs and treatments for prominent diseases in India. This also includes tuberculosis, and the increasing emphasis on stronger antibiotic drug regimens. It also led to the emergence of the TB Association of India in 1939, and later the National TB Control Programme in 1962-1963 (now the Revised National TB Control Program to address disparities and deficiencies in the original program).

However, this social and medical shift also spelled problems for the control of TB in India. It opened the possibilities for usual patient non-adherence to treatments (due to indiscipline or insufficient resources and education), drug resistance, and major anxiety about the recency of treatments and their efficacies. The initially popular Bacillus-Calmette-Guerin vaccine for TB became ineffective in 1979, while the spread of human immunodeficiency virus (HIV) in India in 1984 and development of drug resistant strains of TB in 1992 spelled further trouble for those suffering from TB symptoms.These expose the deep sociocultural barriers and disparities present in various Indian communities, which exacerbate the toll TB is taking on the Indian populace with regards to the rampant antimicrobial resistance.

Sociocultural

Arguably the largest factor about the current spread of MDR-TB in India is the influence of sociocultural factors. This is true to a capacity for essentially any disease, but this has been recognized by the current medical community mainly for diseases relating to mental health and wellness, while these same factors that apply to infectious diseases with physical symptoms have been overlooked by much of this mainstream medical community, which tends to focus mostly on the biomedical aspect of these diseases. In the case of tuberculosis, even with all of its physical symptoms like coughing, sputum production, and fatigue, there are extensive cultural habits, beliefs, and practices especially prevalent in India that can be attributed to the exacerbation of certain virulence factors and creating perfect environments for maximum infection and worsening of symptoms.

The rampant medicalization in the modern world, comprised of larger focuses on biomedical aspects without consideration of sociocultural, economic, and other external factors, has led to large cultural shifts in India especially, with more urban participation in biomedical treatment regimens like Direct Observation Therapy, Short-Course (DOTS) being a largely popular treatment option. This treatment option involves regular supervision of TB patients from medical professionals (mainly to ensure treatment adherence) as they take complex doses of specific medications as a multidrug treatment, common among patients whose TB strains have gained resistance. The degree to which these kinds of programs succeed in India vary strongly by unique state funding and political support, but over the years, DOTS has become the main option for a lot of Indian citizens, with over 12 million TB patients using DOTS since the program’ s inception. However, to understand the true sociocultural and anthropological concepts underlying these issues, we need to go over some basic theories.

The concepts of illness and disease, while sounding similar, are defined differently in the medical anthropology field. Illness describes a patient’ s sociocultural experience of disrupted health, characterized by physical symptoms (like a fever or sore throat), or psychological symptoms (like missing out on a vacation with friends), meaning that illnesses are not confined in only the mental, psychological, or physical space. For example, the flu, a disease caused by the influenza virus, portrays these same aspects; an affected individual has physical symptoms like cold and runny nose as well as psychological and mental symptoms like intrinsic feeling of weariness separate from the physical malaise that the flu is known to cause. However, disease is confined to only physical illnesses and biological abnormalities, like a viral infection. It is the illness which can validly have real consequences and effects on both social dynamics and biological health, while the term “disease ” can really only be utilized to describe an ailment with physical symptoms. It is the misuse and misinformation, along with potential for social manipulation, of the definitions of these two terms that set the foundation for the underlying sociological dynamics surrounding India ’ s public health and tuberculosis situation.

Especially in the case of India, social norms and cultural practices often exacerbate and amplify this stigma and these negative social dynamics. In multiple communities, cultural dynamics and disparities continue to alienate TB patients even with current efforts by the government to reduce the incidence of TB. While the government may be dealing with the physical, biological problem of tuberculosis, not much is being done to address its persistent social impacts. Cultural beliefs and practices of citizens

According to a health care providers handbook developed by the Montgomery County Office of Community Partnerships and the Asian Pacific American Advisory Group in Montgomery County, Maryland, multiple considerations into religious, cultural, and ethnic beliefs must be taken in healthcare settings. Some cultural beliefs listed include a steadfast belief in cleanliness and bathing, higher power granted to elders of families for decision-making, occasional reliance on traditional home remedies based in Ayurvedic medicine, and a severe aversion to cow and pig materials due to religious reasons.

Though this pamphlet describes appropriate treatment strategies and ways to approach the health of Hindu patients, this applies fairly well to Indian Hindu citizens due to having the same foundational beliefs, practices, and worldviews (Queensland Health Multicultural Services, 2011). However, whatever is identical in theory may not be identical in social and physical symptoms. Some of these beliefs, including this belief in cleanliness, may not be able to be fully carried out due to inherent vulnerabilities in India surrounding unclean facilities and resources that may make it theoretically impossible to fulfill these things. In many rural communities, taking a bath may constitute bathing and submerging oneself in a holy river or nearby lake, but many of these communities may have unclean water and unsanitary facilities for this activity, resulting in inadvertent bodily contamination in the guise of an important cultural practice and belief in cleanliness.

Ayurvedic medicine, the main native-Indian medical and cultural belief and practice system based on Hindu tenets, is centered around natural materials like herbs, spices, and other plants typically found in South Asian regions, is not a proven legitimate alternative to allopathic medicine, though it shows much promise nonetheless. Due to the uncertainty of value and effectiveness of this medicine, Indian patients, especially older ones, may have a natural preference for Ayurvedic medicine, which could have an impact on the effectiveness of their treatment (if they do ultimately opt for Ayurvedic medicine), or have a psychological impact on the manner in which they utilize allopathic medicine (since they may not fully believe in it).

Most importantly, most Hindu patients view all illnesses as containing a biological, psychological, and spiritual element, often attaching a stigma to mental illness and cognitive dysfunction in particular.

This stigma results in illnesses being considered as karma for misdeeds in a past life, along with the concept of the evil eye (which is usually attributed to being a cause of mental or physical illness). These kinds of stigmas, especially amplified in rural communities, often lead to social ostracization from friend groups and extended families, which can lead to isolation and a real belief of being punished by a religious power.

The nuance doesn’t stop there. While a lack of emphasis on biomedical knowledge could definitely end up badly with social ostracization, medicalization can shake up the entire dynamic. This time, because an illness is related to an actual explainable biological problem, people tend to start avoiding affected individuals and refuse to reach out for social connections or social gathering to help accommodate the individual in the community; essentially, it is an internal exile from society that occurs.

These social conditions and issues are further exacerbated through the specific social dynamics present in care centers and hospitals in both rural and urban India. In fact, doctor-patient dynamics aren’t instrumental to just Indian TB, but to any health condition in any country. And it’ s been mainly due to American medical influence.

For example, Ethan Watters, through his New York Times article The Americanization of Mental Illness, talked about Dr. Sing Lee, a Hong Kong doctor who witnessed the moment when anorexia hit China; before the Western media could describe it, Chinese locals believed that anorexia, like multiple other physical diseases, wasn’t really connected to fat phobia, and not many reports of fat phobia came out initially (Watters, 2010). However, once the local Hong Kong population Western media connected anorexia to fat phobia, the number of reports on fat phobia in Hong Kong skyrocketed (not because there was fat phobia in the first place, but because the perception of individuals ’ health changed due to exertion of social control by the Western media).

This Americanization of illness in general continues to affect Indian tuberculosis, especially through doctor interactions. When an Indian patient visits a doctor from a high-profile medical institute or hospital, the expectation is that prescription medication and biomedical treatments will be given, due to recent Westernization of global medicine. However, the same is not true as to when a patient visits a local clinic or uncertified care provider like a Ayurvedic medicine guru in villages; in this case, patients usually expect local, homely treatments like simple spices, herbs, fruits and vegetables, and more ordinary forms of medicine rather than prescription medication.

The differences don’t stop there. The social dynamics run so deep that even the expectations for quality of care are influenced across backgrounds. Patients may expect allopathic medicine doctors from wealthier, more well-organized areas to be of higher quality, while they may also expect local healers to have less quality health (though they may go to the healer aware of this and ready to take the risk unless they truly believe in alternative forms of medicine). This just goes to show the extent to which the Western world has influenced medicine in India, and it’ s impacting tuberculosis very much.

Often with diseases like tuberculosis, mortality statistics are assumed to be directly related to medical measures and advancements directly taken by national governments to decrease incidence of a particular disease or illness. However, this is not always the case. Around the twentieth century, there was a growing discussion in the scientific community regarding the questionable contribution of medical measures and medical service expansions to a recent decline in mortality rates; this was especially seen with the decline of smallpox in Britain, with people believing that the invention of smallpox inoculation helped eradicate it. While the smallpox inoculation did play a large role in curbing smallpox cases, improvements in environment were also pointed out, mainly by Habakkuk and McKeown, especially focusing on rising standards of living (mainly in diet), hygiene improvements, and a favorable trend in the relationship between microorganisms and their human hosts.

Since 75% of the decline in mortality rates in the 20th century were associated with infectious diseases, there can be three primary influences: medical measures and immunization, reduced exposure, and improved nutrition. In the graph below (citation: McKinlay), this effect has been largely shown between men and women in the US.

Source: Medical Measures and the Decline of Mortality (McKinlay, 2013)

In addition, most of the mortality decline is from a decline in infectious diseases, so medical measures have usually been focused on this instead of other causes of mortality like heart disease, cancer, and other conditions. This further reinforces the fact that medication and biomedical advancements weren’t the chief agents that caused the massive drop in reduction in the 20th century. Especially as can be seen with tuberculosis in the graphs shown below for the nine most common infectious diseases, the first powerful and reliable drug for tuberculosis, isoniazid, came out around 1950, but the mortality rate associated with TB was already decreasing significantly by that time (McKinlay, 2013).

Source: Medical Measures and the Decline of Mortality (McKinlay, 2013)

So because these medical measures contributed little to the overall decline in mortality for the US, this data can be extrapolated and generalized for tuberculosis in India as well. There is, in reality, a much larger emphasis on cultural contexts, practices, and beliefs through this concept than biomedical interventions when it comes to tuberculosis rates in India. Therefore, medicalization, by amplifying the need to focus on the biomedical aspect, is indirectly hurting efforts to control tuberculosis long-term while risking to increase resistance to dangerous levels.

This rampant medicalization in the modern world has led to large cultural shifts in India especially, with more urban participation in biomedical treatment regimens like Direct Observation Therapy, Short-Course (DOTS) being a largely popular treatment option, involving regular supervision of TB patients from medical professionals to ensure treatment adherence. The degree to which these kinds of programs succeed in India vary strongly by unique state funding and political support, but over the years, DOTS has become the main option for a lot of Indian citizens, with over 12 million TB patients using DOTS since the program’ s inception.

DOTS has a lot of social nuances to it. The concept, involving supervision and encouragement from medical professionals to take large, consistent regimens of medication to fight TB (the prescriptions grow larger as TB becomes more resistant), may seem theoretically sound, but practically, it’ s more complicated than that. The main complaint with DOTS has been the social connection between the medical provider and patient. If the medical provider is a distinguished health professional or doctor while the patient is a rural patient, there may not be much trust and connection immediately that may guarantee a consistent adherence to the treatment regimen. However, a local healer facilitating the DOTS process may have much more success due to greater familiarity and connection and trust. This, as will be discussed later, can only be achieved through regulation of the private sector, which has so far been a huge missed opportunity for the Indian government.

Socioeconomic and political economy

Just as there are sociocultural disparities and nuances with the way healthcare resources are utilized for tuberculosis treatment, socioeconomic gaps and political influence reign supreme in determining the way the Indian public health system deals with MDR-TB. However, some major economic drivers and players need to be examined to first get a grasp on the scope of the issue at hand.

As discussed earlier, the newfound citizen medical and health autonomy has come in recent times with a stronger pharmaceutical sector. The Indian pharmaceutical sector is one of the most popular and sought-after markets in the world, and it’ s very easy to see why it’ s called the “Pharmacy of the World” . With over 10,500 manufacturing facilities, this sector, the 3rd largest (by volume) and 14th largest (by value) global provider of generic drugs, is mainly used for aspects of global medicine like affordable vaccines and treatments; this has been so well done that India is known for giving low-cost, high-quality medicines to its citizens and to other countries receiving Indian imports. This cost efficiency and innovation has greatly enhanced India ’ s GDP and improved healthcare outcomes for diseases like tuberculosis.

According to the Indian Brand Equity Foundation (IBEF), as of 2024, the Indian pharmaceutical market was worth 65 billion USD and is expected to reach a valuation of 130 billion USD by 2030 and a valuation of 450 billion USD by 2047. In addition, India has the largest number of USFDA-compliant pharmaceutical plants outside the US, along with over 2,000 World Health Organization Good Manufacturing Practices (WHO-GMP) approved facilities with more than 10,500 facilities in more than 150 countries. These statistics continue to show the sheer dominance, reliability, and influence India holds in the global pharmaceutical market. This ultimately has many effects towards the national economy.

According to the International Monetary Fund (IMF) DataMapper and other recent data from the Indian government, the Drugs and Pharmaceuticals Industry has a large 1.72% contribution to the national GDP (Make in India, 2025). In addition, a trade surplus (meaning more pharmaceutical goods have been exported rather than imported, which increases GDP contribution) has also been maintained since 2010, with an annual trade surplus of about $13.10 billion USD in the 2018-2019 year range. The industry has also received a cumulative FDI (foreign direct investment) of about $16.5 billion USD from April 2000 to March 2020, showing its appeal and potential for further outside investment. Distribution of drugs via the pharmaceutical sector is achieved through multiple health care centers and health-based microbusinesses, mainly prevalent in multiple population-dense areas and making up nearly 30% of India ’ s GDP (Aftab, 2024).

The scope and grandeur of the Indian pharmaceutical industry has so far been conveyed with the above economic statistics and information. However, with a densely populated country like India, problems and socioeconomic disparities are bound to occur with how the pharmaceutical sector transfers and communicates health information and medication to the public, with both urban and rural areas having numerous issues regarding this.

When it comes to the quality of healers, it was already mentioned earlier that perceived higher quality healers, which tend to be more professional healthcare providers from the biomedical sector, are seen more favorably by the expectations of patients than perceived lower quality local healers. In addition to this, as may be obvious, these higher quality healers tend to be more expensive and may be inaccessible to poorer individuals (of which there are many in rural areas), while lower quality healers may be the first choice due to cost efficiency. However, this relationship between quality of care and socioeconomic standing greatly widens the wealth and health gap, as poorer individuals tend to have worse health outcomes with TB than wealthy individuals, all because of class differences between local healers and more high-profile health professionals in relatively large hospitals.

This can also be seen with DOTS, as it was already mentioned earlier that DOTS tends to be more successful if trust and connection is there between patient and health provider; this tends to be truer if a wealthy patient connects with a health professional while a poor patient might connect better with a local healer (again, there will be a difference in quality of care if this occurs, and it may not look good). Therefore, it can be said that higher quality DOTS is more available and viable to individuals in high socioeconomic standing and quality of living, while the opposite is true for lower socioeconomic standing, which may not get proper DOTS treatment from local healers, especially considering the lack of governmental regulation of the private sector of health.

The simple solution to this, one could say, is to meaningfully expand higher quality DOTS care, medication, and health resources to poorer parts of the country. However, an expansion of care, testing units and areas, treatments, and appropriate medical expertise to more rural areas of India while keeping consistency of good quality is incredibly difficult and costly; this is especially true for India, the world’ s most populated nation. Costs for the Central TB Division, the main governmental department dealing with the control and reduction of tuberculosis cases through the National Tuberculosis Elimination Programme (NTEP), have risen from $76 million USD from 2016 to nearly $2.5 billion USD, reflecting India ’ s promise to eradicate tuberculosis by 2025, but this still continues to fall short of their goal of a 2.5% GDP budget allocation, though this may change in the coming years. Additionally, the possibility of false data reporting, internal corruption, and underrepresentation among numerous regions threatens to derail these seemingly promising statistics. Just the baseline upscaling, without even factoring in DOTS and medical private sector regulation, is already costly and not meeting its GDP allocation goals so far, showing that if India wants to upscale its TB testing and treatment centers without sacrificing quality, a shift in the baseline system is necessary.

Biomedical/Biological/Biochemical

Multidrug-resistant tuberculosis (MDR-TB), harboring much of the same symptoms of regular tuberculosis, including fever, chest pain, general weakness, cough, and sputum production, is a more dangerous form of TB, showing large amounts of resistance to major drug classes and products including rifampicin and isoniazid, both commonly used and powerful first-line drugs to treat TB that are now obsolete to treat MDR-TB. This drug resistance, as mentioned earlier in the introduction, is caused by increasing numbers of efflux pumps in MDR-TB cells that pump out antibiotic drugs intended to kill the pathogen and more enzymes that inactivate drugs like rifampicin and isoniazid. As already discussed, patient and sole care-related factors that may exacerbate antimicrobial resistance include inappropriate use of TB drugs and formulations along with premature treatment interruption, causing drug resistance.

Currently, multiple prescription drugs that can treat multidrug-resistant tuberculosis now are in good supply, although the pathogen can threaten these drugs too if resistance goes unchecked into the future. These include second-line drugs like levofloxacin, moxifloxacin (both of which are fluoroquinolones), and combination regimens that include drugs like moxifloxacin, clofazimine, and ethambutol, among others. These combination regimens are commonly used in Direct Observation Therapy Short-Course (earlier described as DOTS), which has had growing global success but still suffers the risk of patient indiscipline and misinformation. This risk, while negligible in the first few decades since antibiotics were introduced to treat tuberculosis due to their great strength, has now become relatively larger, making those same antibiotics powerless against modern infections. Due to this growing resistance, it is imperative for affected nations to focus on widespread access for testing and treatments; in India specifically, as will be discussed in a later section in more detail, the Central TB Division is now hoping to do this.

The actual process of drug resistance is quite complex. Tuberculosis drug resistance occurs when the bacteria that cause TB, Mycobacterium tuberculosis, develop mutations (or are transferred genetic material from other bacteria with resistance genes) that allow them to survive despite the use of anti-TB drugs. These mutations usually become a problem when treatment is not properly followed, as already discussed. For example, mutations in the katG or inhA genes make the bacteria resistant to isoniazid, while mutations in the rpoB gene cause resistance to rifampin, both of which are the top-line drugs to treat tuberculosis. When the bacteria become resistant to both, the condition is called multidrug-resistant TB (MDR-TB). If resistance extends to second-line drugs like fluoroquinolones or injectables, it is called extensively drug-resistant TB (XDR-TB). If the process goes even further and the TB pathogen somehow becomes resistant to all drugs (meaning it is practically impossible to treat with medication), it is called pan-drug-resistant TB (PDR-TB); while there haven’t been much cases of PDR-TB yet, it still remains a looming fear on the horizon should the global public health system continue to neglect drug resistance.

To combat these threats of multidrug-resistance and extensive drug resistance, global public health systems and the modern medical community are focusing a lot on biomedical treatments like novel drug development, new drug therapies, possible applications of immunotherapy, and more. This emphasis on medical conditions, while important, has also come at the expense of neglecting relevant external issues relating to regional cultures, socioeconomic disparities, and other topics that are listed in this paper. While biomedical treatments have been emphasized (especially in India, where , there has been a relatively lack of concern for these conditions, which may include poor living conditions, unsanitary resources (water, food, air), lack of sanitary protocol in everyday life (for example, lack of handwashing), and even certain cultural practices and beliefs that may inadvertently cause this (as was already discussed in a previous section).

Over time, Indian biomedical treatments themselves have changed in efficacy towards treating TB (Ministry of Health and Family Welfare; Govt. of India, 2022). Mass Bacillus-Calmette-Guerin (BCG) vaccine campaigns started in India in 1951, but soon proved ineffective in the 1990s, especially against TB strains in India that had grown more resistant and more strongly attacked the lungs of the victim. Shortly after these campaigns, there was a notable shift towards home-based chemotherapy, employing many of the same drugs that are used today to treat tuberculosis; however, access to these drugs varied in the initial days, and access only got strengthened following the rapid growth of India ’ s pharmaceutical market, which was discussed earlier. The treatments and testing methods for TB, while advanced, are relatively costly and hard to implement; a regular DOTS regimen

As mentioned earlier in the introduction, MDR-TB has proven to be a huge problem over the past few years for the country ’ s public health. The impacts have gotten worse since its discovery in 2012 in a Mumbai hospital, explained by the fact that India continues to have 26% of global TB cases as of 2023 according to the National Institutes of Health (NIH). It has become a public health crisis , as this 26% involves 8.2 million people diagnosed with tuberculosis, 1.23 million of those people dying that year (Mandal, Rao, Joshi, 2023).

While this discussion on the biological and social issues and influential factors related to the current case of MDR-TB has been far-reaching, these factors tend to be caused by underlying flaws in global health systems.

Systemic Flaws

A large part of these discrepancies in healthcare, treatment, and true betterment of the afflicted when it comes to MDR-TB in India is due to the underlying public health system of India. There are multiple flaws with the Indian healthcare system when it started to handle the tuberculosis (and later the rising resistance of later strains), and a lot of it has to do with the main government department tasked with controlling the spread of TB: the Central TB Division, carrying out the the National Tuberculosis Elimination Programme (formerly called the Revised National Tuberculosis Control Programme).

The emphasis in this program largely paints MDR-TB as a public problem, which it essentially is. Usually, the government should ideally ensure public action, not necessarily the individuals, but this may need to change in the future, as the public needs help from experts, advocates, pressure groups, and lobbyists to represent their perspectives and interests (which maybe are not being considered by the Central TB Division currently). This conveys that multiple individual actors in Indian society, while having the potential to influence health policies sociopolitically, are usually experiencing a power imbalance, with higher-status actors having more power to influence unlike lower-status actors like the enormous Indian middle class. Systematically speaking, inclusion of all local groups of actors, including public health practitioners, health planners, policy makers, and patients themselves, might seem impractical from a financial and economic standpoint but it is absolutely necessary for this form of equity to show when constructing a public health system.

Additionally, funding tends to gravitate towards the political and medical interests (which tend to be more high-paying and lucrative), which affect the health decisions the Central TB Division takes. This is especially true in defining TB, exerting medical social control over the concept of the disease. This fascinating social dynamic leads to an interesting clash: should we keep the Central TB Division (basically the government) or the vocal actors (that bring in important perspectives, like private practitioners, non-governmental organizations, and researchers) out of the limelight.

Weak data exists for the TB epidemic, as there was a lack of data from the unregulated and diversified private sector (more on this later). When a large TB epidemic sprouted up in 2013, the government took data on a few hospitals in Gujarat and Chennai over the course of a few weeks, hoping to extrapolate and adjust these numbers to represent the whole nation. The data showed 1-3% MDR-TB in Gujarat and Chennai, with 13-17% resistance in previously treated cases. In addition, 3% of TB patients in the Gujarat and Chennai studies are considered to have native MDR-TB (in other words, they had it already when they came into the hospital), while 17% of TB patients were considered to have acquired MDR-TB (meaning they most probably acquired the strain during their hospital stay). This continues to show how drug resistance is especially opportunistic in nosocomial, or hospital-acquired, infections.

Of course, even when assuming that the Central TB Division honestly collected the data as best they could, there are still general epistemological questions to be asked when considering the validity of the data as a whole. For example, mortality statistics may be inadequate; according to sociologist Dr. John B. McKinlay, many conditions may be responsible for deaths (and not just the one that the patient came to the hospital with). In addition, changes in disease classifications and social norms and expectations of health illnesses can also negatively influence these statistics (for example, death by epilepsy might have been perceived as negative spiritual outbursts in an earlier time). However, we should still be able to measure those limitations and hopefully account for them, especially considering these limitations may apply equally to all studies involving mortality stats, especially ones involving TB hospital deaths.

However, critics have a different outlook. Looking at the fishy nature of these numbers and statistics, they feel that the government is not facing up to the problem’ s scope, exaggerating overly optimistic TB data that may give a false sense of security when a 3% nationally-extrapolated rate of MDR-TB, doubting whether the Gujarat and Chennai studies were even representative of the total MDR-TB numbers. In fact, an unnamed microbiologist in these studies mentioned that the government doesn’t like to see high numbers in MDR-TB rates, and therefore the political pressure is on to keep the numbers low (whether it was actually achieved or not). Due to this, most critics are in favor of more rationality and quality of innovations to properly map MDR-TB and bring transparency with the public.

While some of this has already been mentioned in the sociocultural factors section of this paper, multiple drawbacks in the system lead to a lot of discrepancies in the health infrastructure of health facilities like hospitals and clinics. A lot of cases (according to the paper linked at the top of the section) occur due to mismanagement and poor treatment; many times, it ends up being in the hands of the patients, but also can stem from the health professionals in establishments. Central TB Division officers label MDR-TB as a problem created by external factors and the actors themselves (due to lack of regulation and mistreatment at the most direct level, not to mention nonadherent patients). However, critics argue that this is just a narrative pushed by the program to hide its own shortcomings. In reality, this ends up being a little bit of both; while drug pressure does exist with growing strength of TB with each infection and higher malnutrition of a country (making a better “playing ground”).

To add on, DOTS and other standard current TB treatments can also fail if improper direct supervision and little cooperation with the private sector occurs. The private sector, consisting of about 63 million microbusinesses (with over 10,000 of those microbusinesses as recognized health organizations), is probably the most large and far-reaching influential organizational entity in India, across both urban and rural areas (although they do tend to be way more concentrated in urban areas). One issue with the current health system is a noticeable lack of communication and coordination with the private sector, which can lead to many sometimes unscrupulous local healthcare workers deliver improper treatments and drugs to TB patients and may not report proper numbers, distorting the true validity of current data and the effectiveness of the program. If the private sector ends up being regulated by the Central TB Division, multiple local healers and ethnomedical professionals can be held accountable while also having their voices heard on possible holistic treatments, leading to breakthroughs in TB treatment and curbing the rise of resistance.

Conclusion, and Suggestions For the Way Forward

Addressing the growing MDR-TB crisis, in summary, will need a lot more avenues of research and problem-solving than the current steps and solutions being devised to merely keep it at bay. The high emphasis on the biomedical aspects of tuberculosis in India (in general) is unfortunately masking the equally important sociocultural aspects and phenomena that occur with Indian tuberculosis. Therefore, to address these aspects as well, an integrated medical approach is needed; the medical community should not only address the biomedical aspects of tuberculosis, but also take into account the sociocultural and economic aspects which are arguably equally important in vulnerable areas like India.

However, this is easier said than done when trying to scale the full scope of this ambition. However, apart from making necessary changes to the Indian public health system, a great starting point is to build cultural competency and sensitivity with Indian patients, no matter the health professionals ’ qualifications, degree, or amount of knowledge. Respecting the patients ’ perspectives, and smoothly guiding them in the right direction with their cultural beliefs about TB and the appropriate hybrid treatment plans that can combine Ayurvedic medicine and allopathic medicine with alleviations to social conditions, can ultimately result in a more culturally respectful environment in multiple rural and religiously devoted regions that can holistically address TB’ s rising antimicrobial resistance. It can also help break stereotypes commonly associated with the healthcare field, various types of health professionals and treatments, and personal psychological evaluations about one ’ s own health.

In a time when systemic and socioeconomic discrepancies have exacerbated the destructive nature of the recent COVID-19 pandemic in multiple countries, these disparities can serve as a learning moment for India and its Central TB Division to improve their main public health system, mode of testing, cost-effectiveness, reach, and sociocultural sensitivity. Tuberculosis is a curable disease, and yet it is still the most prevalent infectious disease in India to this day; hopefully that changes soon.

Bibliography

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Mandal, S., Rao, R., & Joshi, R. (2023b, March 24). Estimating the burden of tuberculosis in India: A modelling study. Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine. https://pmc.ncbi.nlm.nih.gov/articles/PMC10353668/#:~:text=We%20estimated%20total%20 TB%20incidence,was%2023%20and%2027%20respectively

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About the author

Akshar Belaguly

Akshar is currently a freshman at Brown University concentrating in Biochemistry and Molecular Biology and wrote the paper while he was a senior at Gretchen Whitney High School in Cerritos, California. Some of his academic interests include biochemistry, genetics, and analytical chemistry, but he also has a deep fascination with medical anthropology that will hopefully give him holistic perspectives in his journey to medical school.

In addition, Akshar has also been part of his school’s Science Olympiad team, loves to watch and play cricket and basketball, and loves to spend time with his family in his free time.

The post The Cultural Influences of Medicalization: How Culture Influences Tuberculosis In India appeared first on Exploratio Journal.

Abstract

Muscle fatigue is a critical physiological condition that limits physical performance and impacts overall health. While commonly experienced during intense activity, the chemical processes driving fatigue are often overlooked. This paper explores the molecular mechanisms underlying muscular exhaustion, including neurotransmitter imbalances, disruptions in energy metabolism, and calcium regulation failures. By examining the complex processes that result in muscle fatigue, such as glycolysis, byproduct accumulation, and E-C coupling, this paper highlights how biochemical changes affect muscle function. Additionally, strategies such as buffering with sodium bicarbonate to delay fatigue offer insight into potential solutions, thereby enhancing performance. This review first outlines the biological processes that affect muscle fatigue before diving into the deeper chemical aspects of it.

Keywords: fatigue, glycolysis, lactic acid, muscle exhaustion, anaerobic, sodium bicarbonate buffers, E-C coupling, calcium regulation

Introduction

Athletes oftentimes experience severe soreness and slow recovery following a high intensity workout, hindering their ability to perform as usual in the following days. The physiological context of this phenomenon, known as muscle fatigue, was first researched by Angelo Mosso in the late 1800s, who demonstrated how while exercise increases endurance and muscular strength, it simultaneously extends fatigue. He was the first to describe the chemical process behind this fatigue, attributing it to toxic substances and acid. In 1891, he eventually published the paper “La Fatica” (Fatigue), which included a formulation of laws that described the causes of exhaustion. Presently, further research has been established to expand on the cellular and molecular mechanism of muscle fatigue and more specific chemical processes than what Mosso explored over a hundred years ago. Muscle fatigue is now defined as the decline in the body’s ability to produce force, and is known to exist as soreness following physical activity or more critically as a result of a chronic condition.

Fatigue and Hyperthermia

Types of Fatigue Muscle

fatigue results from both central and peripheral mechanisms. Central fatigue originates in the central nervous system (CNS) and occurs when the brain’s ability to send signals to the muscles becomes reduced. Peripheral fatigue, on the other hand, originates within the muscle fibres themselves and reflects impairments within the muscle. Fatigue can also be classified as acute, developing from short term exertion, or chronic, persisting over an extended period due to underlying health conditions. Additionally, hyperthermia, which is a state of increased core body temperature, can worsen both types of fatigue by disrupting homeostatic and neurochemical balances. Key neurotransmitters, including serotonin, dopamine, glutamate, and GABA, play significant roles in the development of fatigue during physical activity.

Hyperthermia and its Impact on Fatigue

Central Fatigue and Key Neurotransmitters

One of the most important neurotransmitters involved in the process of central fatigue is serotonin. Serotonin levels increase during exercise due to a rise in free tryptophan, an amino acid that forms serotonin. As fat stores are broken down during exercise, free fatty acids displace tryptophan from the protein albumin, allowing more tryptophan to enter the brain. There, it is converted into serotonin. High levels of serotonin are linked to sensations of lethargy and reduced motor function. This occurs when serotonin binds to specific receptors (such as 5-HT1A) that inhibit muscle activation once they are overstimulated.

Dopamine, another key neurotransmitter, works in opposition to serotonin in many ways. Dopamine is responsible for maintaining motivation and alertness, both of which are essential for continued physical performance. It is made from the amino acid tyrosine and supports sustained motor output. When dopamine levels are low, central fatigue is more likely to occur. However, regular physical training can increase dopamine synthesis and receptor activity, improving an individual’s resistance to fatigue over time.

Glutamate, the brain’s primary excitatory neurotransmitter, also contributes to central fatigue. Normally glutamate levels are tightly controlled by transporter proteins such as GLT-1. However, intense exercise can impair the function of these transporters, allowing glutamate to build up on the outside of nerve cells. This can disrupt communication between neurons and potentially lead to neurotoxic effects. Additionally, glutamate plays a role in the production of lactate by brain cells, which helps supply energy. If glutamate is not properly regulated, it can affect both brain signaling and energy metabolism, further promoting fatigue.

GABA (gamma-aminobutyric acid) is the main inhibitory neurotransmitter in the CNS. During exercise, GABA levels rise, especially in the sensorimotor cortex. This increase is linked to higher blood lactate levels, suggesting a connection between muscle metabolism and brain chemistry. Elevated GABA activity can reduce the brain’s ability to sustain motor output, leading to the perception of fatigue and a decline in performance.

Peripheral Fatigue

Peripheral fatigue occurs when there are changes inside the muscle that interfere with its ability to contract efficiency. These changes often include the buildup of byproducts like H+ ions, inorganic phosphate, and reactive oxygen species, all of which can reduce the effectiveness of muscle contractions. Metabolic acidosis, caused by a drop in pH, weakens the interactions between actin and myosin, the proteins responsible for muscle contraction. At the same time, depletion of stored energy molecules like ATP and glycogen reduce the muscle’s ability to generate force.

Effect of Hyperthermia on Central and Peripheral Fatigue

Hyperthermia acts as a catalyst that intensifies both central and peripheral fatigue by simultaneously disrupting brain and muscle function. When core body temperature rises above approximately 40°C, brain temperature also increases, which can interfere with the hypothalamus and reduce the brain’s ability to send signals to the muscles. This effect on the CNS becomes especially noticeable during prolonged exercise, leading to a drop in endurance and lower motor unit activation. At the same time, hyperthermia stresses the cardiovascular system, as more blood is sent to the skin to release heat. This reduces the amount of blood and oxygen reaching active muscles, pushing them to rely more on anaerobic metabolism. As a result, lactate and H+ ions build up, resulting in peripheral fatigue. Heat also interferes with energy production in muscle cells, making contractions less effective. Together, these effects cause fatigue to set in faster and more severely, especially in hot environments or during physical exercise.

Energy Depletion: Glycolysis

Muscle fatigue is driven by disruptions in ATP availability, particularly when glycolysis becomes the primary energy source during prolonged physical activity. Glycolysis converts glucose to pyruvate, producing ATP rapidly but in limited amounts. As glycogen, the primary substrate for glycolysis, is depleted, ATP synthesis declines, weakening critical energy-dependent processes within the muscle fiber. This process is shown below by Figure 1:

Figure 1: Blood glucose and muscle glycogen provide glucose for glycolysis, producing ATP . With oxygen, pyruvate enters aerobic respiration. Without the presence of oxygen, pyruvate is converted to lactic acid, which enters the bloodstream (Betts et al., 2013)

One of the most affected systems is excitation-contraction (E-C) coupling, which links electrical signals to mechanical contraction. This process relies on ATP to fuel the sarcoplasmic reticulum (SR) Ca2+-ATPase, which pumps calcium back into the SR, and for cross-bridge cycling between actin and myosin, the proteins responsible for muscle contraction. Glycogen stored near the SR, particularly in intermyofibrillar regions, plays a key role in sustaining ATP levels. Depletion of this glycogen pool has been shown to reduce SR calcium release, disrupting calcium signaling and weakening muscle contraction even when total cellular ATP is maintained.

Consequences of Anaerobic Metabolism in Muscle Fatigue

Intracellular Acidosis and pH Imbalance

Accumulation of Lactic Acid and H+

Intense exercise results in the body having to make energy without oxygen, leading to the accumulation of lactic acid and hydrogen ions in the muscles. During high intensity exercise, the energy consumption of the body’s skeletal muscle cells increases to compensate for what is released. The majority of this Adenosine triphosphate (ATP) comes from anaerobic metabolism, a process which utilizes the breakdown of glycogen into lactic acid to generate ATP at a quicker rate. The anaerobic glycogen breakdown differs from the normal aerobic pathway due to the lack of oxygen available during the process. Initially, the glycogen goes through glycolysis (see section “Energy Depletion: Glycolysis”), which produces pyruvate and a minimal amount of ATP. Aerobic respiration utilizes oxygen to produce substantial amounts of ATP, as the produced pyruvate moves into the mitochondria and produces CO2, H2O, and ATP. In the anaerobic process, the pyruvate is instead converted to lactic acid (C3H6O3) through the lactate dehydrogenase enzyme. Lactic acid is a colorless compound which exists in two active forms, dextro-lactic acid and levo-lactic acid and can occur in the blood, muscles, or organs.

When lactic acid accumulates, it dissociates into lactate and H+ (see Figure 2). The dissociation of lactic acid accumulates H+ , increasing [H+] and therefore reducing pH, as pH is the -log[H+] and is inversely related to the concentration of H+ . The drop in the pH of blood during exercise impairs muscle function and the body’s ability to contract efficiently. In the past, the accumulation of lactic acid was widely considered the main cause of muscle fatigue, but recent studies have attributed the fatigue more to the pH’s effect on the resynthesis of phosphocreatine, rather than a direct effect of the lactic acid.

The Role of Phosphocreatine in Muscle Fatigue

Accumulation of Inorganic Phosphate

Anaerobic metabolism additionally utilizes phosphocreatine as an anaerobic energy system to speed up the process of ATP generation, as it is able to provide a burst of energy by transferring a phosphate group to ADP (stored energy), forming ATP. This reaction is catalyzed by the enzyme creatine kinase (CK), and is demonstrated by the image below:

The equation PCr + ADP → ATP + Cr (see Figure 3) displays how the energy that is liberated from the hydrolysis of the phosphocreatine is used to synthesize ATP when the Pi bonds to the ADP. The breakdown of phosphocreatine to inorganic phosphate and creatine can be displayed by the net ionic equation:

PCr → Pi + Cr

The accumulation of inorganic phosphate can depress contractile function and increase muscle fatigue through the formation of calcium phosphate and its effect on our body’s Ca2+ release.

Formation of Calcium Phosphate and Ca2+ Release

The inorganic phosphate formed by the hydrolyzation of phosphocreatine moves from the myoplasm (the cytoplasm of the muscles) to the sarcoplasmic reticulum (SR), a type of reticulum within muscle cells that is responsible for storing and releasing Ca2+ ions and stabilizing calcium ion concentrations. In normal muscle contractions, when muscle fiber is stimulated, the SR releases calcium ions into the cytosol of the cell, allowing the ions to bind to muscle fibers, and triggering muscle contraction. During muscle fatigue, the Pi ions bind to the Ca2+ ions, resulting in the formation of calcium phosphate (CaPi). Due to this, the number of calcium ions available to release reduces, and therefore, the sarcoplasmic reticulum’s ability to efficiently release and uptake Ca2+ is compromised. With a decline in the amount of Ca2+ available for muscle contraction, the body’s ability to generate force is much lower.

Additionally, the decrease in pH in the blood during high intensity exercise (see section “Accumulation of Lactic Acid and pH”) can disrupt the initial process where Ca2+ binds to muscle fibers and triggers contraction in the SR. The surplus of hydrogen ions caused by the accumulation of lactic acid can displace calcium ions from binding sites, where it would otherwise bind with proteins such as troponin C, and allow for normal muscle contraction. The combination of the effect of low pH on the functions of the SR and the effects of the phosphocreatine from the anaerobic process decrease muscle force and power output, resulting in muscle fatigue.

During recovery from high intensity exercise, the sarcoplasmic reticulum uses its Ca2+-ATPase pumps to reabsorb Ca2+ , and ensure relaxation, as shown in Figure 4. Recovery from endurance training, which requires slow-twitch muscles slightly differs from recovery from rapid muscle contractions that require fast-twitch muscle fibers. Due to their higher affinity for calcium transportation, slow-twitch muscle fibers can more efficiently pump Ca2+ back into the SR, allowing for faster recovery from endurance activities than strength or speed work. The difference between recovery for sprint and endurance athletes can be largely attributed to the variation in how their differing muscle fiber types deal with calcium transportation.

Sodium Bicarbonate as a Buffer and its Effect on Athletic Performance

In recent years, some athletes, primarily semi-endurance bikers and runners, have begun a practice of intaking sodium bicarbonate (also referred to as baking soda) with water about 1.5 to 2 hours before their race or competition, with the goal of enhancing their performance. HCO3 – , which is present in sodium bicarbonate (NaHCO3), is part of the acid-base buffering system present in human bodies that helps regulate blood pH concentrations. The bicarbonate system is the largest buffer system in the blood. When athletes intake sodium bicarbonate, additional reacts with the excess H+ , a process demonstrated by the chemical equation below:

H+ + HCO3 – ⇌ H2CO3 ⇌ H2O + CO2

According to Le Chatelier’s principle and the common ion effect, this shifts the equation to the right and reduces H+ concentration, therefore slightly raising the pH. This further enhances the buffering effect and thus forth delays the decrease in blood pH that occurs as a result of the excess H+ ions. H2CO3 can be defined as a Brønsted-Lowry acid, as it can donate a proton, while HCO3 – , which accepts a proton, can be defined as its conjugate base. A mixture containing an acid and its conjugate base is a buffer and has the ability to resist drastic changes in pH, so by delaying this change, athletes can delay muscle fatigue and slightly improve their performance. As H2CO3 is unstable, it decomposes into H2O and CO2, which is exhaled through the lungs and also helps regulate blood pH. Sodium bicarbonate has also been shown to influence inorganic phosphate creation, again enhancing performance by allowing Ca2+ ions to bind efficiently, even during intense exercise.

Conclusion

Muscle fatigue is not simply the result of overuse, but a result of many chemical processes that often go unnoticed by athletes and many who are struggling from muscular exhaustion. From ATP depletion and lactic acid buildup to pH imbalance, fatigue reflects a breakdown in the body’s ability to maintain muscle contraction at the cellular level. Continued exploration of muscle biochemistry can further practical applications in medicine and sports, and allow for the development of better treatment and training methods for muscle fatigue.

References

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About the author

Nimeesha Kolari & Radha Panse

Nimeesha Kolari and Radha Panse are currently seniors at Cupertino High School in Cupertino, California. Nimeesha is passionate about chemistry in the context of the human body, and is planning to to study biochemistry in college. In her free time, she enjoys running cross country and track, trying new foods with her friends and family, and walking her dogs.

Radha enjoys biology, chemistry, and mathematics, particularly in areas such as biochemistry and pharmaceutical sciences. Outside of academics, she is a member of the school’s track and field team and enjoys exploring nearby trails, building LEGO creations, and reading in her free time.

The post The Chemistry of Muscle Fatigue: A Review of the Biological and Chemical Processes Behind Muscular Exhaustion appeared first on Exploratio Journal.

Abstract

With more industrial developments, there is an increased demand for clean energy. One source that can provide clean energy is hydrogen-based fuels. In the paper, the term “hydrogen power” means all energy generation methods based on hydrogen, such as hydrogen combustion or hydrogen fuel cells. However, generating hydrogen at an industrial scale requires scaling up hydrogen generation processes such as electrolysis. This depends on selecting suitable catalysts to expedite the process. This paper provides a review of existing theories that help identify potential catalysts for this process. Specifically, d-band theory and spinel theory predict the activity descriptors for Oxygen Evolution Reactions and Hydrogen Evolution Reactions, respectively.

Keywords: water electrolysis, hydrogen evolution reaction, oxygen evolution reaction, catalyst, d-band theory, spinel theory

The current world and social structure depend on generating electricity. There are various approaches to generating electricity, with the main options being using fossil fuels (combustion), renewable energy, and nuclear energy. In most fossil fuel generators, byproducts, mainly in the form of greenhouse gases (GHGs), will be produced due to the combustion reaction, and GHGs are capable of warming up the Earth’s climate and polluting the atmosphere (Markandya & Wilkinson, 2007, p. 979). According to data collected by a research team that published the findings in the journal “Earth System and Scientific Data,” a rigorous academic journal with very transparent processes, which are then compiled by Climate Watch, an organization under the World Resource Institute, the annual GHG emissions from the entire world, measured in billion tons of CO2 equivalent, from 1850 to 2016, it increased from 1.4373 billion tons to 46.50 billion tons or approximately a 3135 % increase in emissions. Most of these emissions come from energy demands (World Resources Institute, 2022). According to the same source, about 33% of the world’s emissions in 2021 came from electricity and heating, the largest sector of global GHG emissions (World Resources Institute, 2022). The data shows that energy production is a considerable portion of the global GHG emissions. Thus, a clear and most impactful solution to climate change will be finding a clean or low-carbon energy source, as it will directly address 30% of global emissions.

There are many ways to produce clean and non-polluting energy, such as solar energy, which is generated directly from sunlight. However, these methods are not perfect. Most renewable energy sources, particularly solar energy, are intermittent or unstable, requiring additional infrastructure to account for the problem (Mathew, 2022, p. 5). This, combined with the lack of a large and powerful energy storage system, leads to grids with renewable sources having to depend on fossil fuels, creating additional GHG emissions (Mathew, 2022, p. 5). Additionally, these renewable energy sources consume many resources, particularly land. For instance, a 1000 MW fossil fuel power plant requires 1-4 km2 of land for the entire facility, while renewables require a lot more land, with solar requiring 20-50 km2, wind requiring 50-150 km2 , and biomass requiring 4000-6000 km2 (Rashad & Hammad, 2000, p. 213). These factors combined make most of the current renewable energy systems unable to generate electricity as effectively as methods like fossil fuel. They could potentially release additional GHGs from the extra land use and infrastructure. However, not all renewable energy sources have that problem, and using hydrogen power can prevent these problems.

Hydrogen has many advantages as an element in itself. It is a highly energy-dense element (in terms of mass), making it comparable with other standard energy production methods, such as fossil fuels like petroleum or coal, as shown in Figure 1 (U.S. Department of Energy, n.d.-b). Hydrogen power has an effective energy system that is proven by fossil fuels. The underlying principle of hydrogen power is the same as that of fossil fuels, converting hydrogen combustion’s thermal energy to steam’s kinetic energy by boiling water, finally pushing a turbine with that kinetic energy, and generating electricity. This process has been proven by decades of application and is widely used today. Approximately 42% of all electricity generation in the United States uses steam turbines (U.S. Energy Information Administration, 2023). Hydrogen also comes with the added benefit of not producing any pollutants when burned, with its byproduct being only water, which is the product of the hydrogen combustion reaction. Not only that, but hydrogen can also be used in fuel cells, which is another way to produce power efficiently, with its efficiency ranging from 40% to 60% (U.S. Department of Energy Energy Efficiency & Renewable Energy, 2010). Hydrogen is also an essential industrial element, as shown in Figure 2, commonly used in industries like agriculture, where it can synthesize ammonia, a key component in all modern fertilizers (WHA International Inc, 2023; World Nuclear Association, 2024).

Background and Literature Review

Despite the benefits of a hydrogen-based energy system, getting hydrogen clean is a challenge. There are various ways to produce hydrogen; however, most are produced using fossil fuels, which release GHGs. In fact, approximately 95% of the world’s hydrogen production is based on fossil fuels and releases GHGs (Rosenow, 2022). In the case of hydrogen power, generating power with hydrogen that has a considerable amount of carbon footprint attached to it during its production process will make the purpose of hydrogen power obsolete, and thus, using renewable or clean hydrogen is essential to ensuring the benefits of hydrogen power can be released at full potential. To better classify different types of hydrogens, color codes are assigned to them, with different colors representing different carbon footprint levels, as shown in Table 1 (National Grid, 2025). Based on the classification of hydrogen, for hydrogen power to be completely carbon-free, using green hydrogen is the best approach. Electrolysis is essential to creating green hydrogen, as detailed in Table 1. It works by splitting water molecules, which consist of two hydrogen atoms and one oxygen atom. Thus, running a specific voltage through the water molecules will break the chemical bond between the atoms and release the atoms themselves. This method is carbon neutral and does not release additional GHGs, assuming the electricity used for the electrolysis is carbon neutral.

In an electrolysis reaction, the electric current passed through serves as the activation energy of the reaction to dissociate water molecules into hydrogen and oxygen. This happens because when an electrical current is passed through the anode, cathode, and the water itself, the water molecules undergo oxidation at the anode, producing oxygen gas and releasing electrons. At the same time, the hydrogen from the oxidation is also reduced at the cathode, where hydrogen ions are being reduced by gaining an electron from the oxidation at the anode, finally creating both hydrogen and oxygen gas at the ends. This process, however, is very energy- intensive as it needs to overcome a strong energy barrier presented by the OH bonds in water. These bonds in water have an average bond energy of 461.5 kJ/mol, an accepted value, which is quite strong (Song & Le, 2013). As a result, for the reaction to occur, more energy has to be passed through, making the process less efficient and more challenging to complete, decreasing the possibility for it to be used in large-scale industrial processes such as generating hydrogen in large enough quantities to supply power plants without a method to decrease the amount of energy used.

As a result, catalysts are being used to lower the amount of energy needed in this process, as a catalyst can lower the activation energy of reactions while not consuming itself during the reaction (U.S. Department of Energy, n.d.-a). This can be used to boost the amount of hydrogen acquired from electricity, improving the efficiency of the electrolysis reaction. There are various types of catalysts with pros and cons, as well as having properties that are more inclined to support either the oxidation or the reduction reaction. In the industry, the key to successfully creating and commercializing the system for broad public use is to find a suitable catalyst that balances various qualities. This can be done by reliably identifying the activity descriptor, which represents a quantifiable indicator for a catalyst’s capability to catalyze a specific reaction, in this case, the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER). Thus, this paper focuses on reliably identifying the activity descriptors for the hydrogen and oxygen evolution in the water electrolysis reaction.

HER and OER

As established before, the HER reduces hydrogen ions, and the OER oxidizes water molecules to release oxygen. Generally speaking, the OER is more energy-intensive and slower than the HER because it has a more complex reaction mechanism. Specifically, there are four electron transfer processes during the OER, as shown in Figure 3. This multi-step process requires breaking the strong OH bond in water to generate oxygen. The four-electron transfer process also means that forming multiple intermediates is challenging (J. Li, 2022). This, in turn, creates kinetic barriers, making it much slower and requiring a higher overpotential (extra energy) to make the reaction happen. On the other hand, the HER only requires 2 electron transfers, meaning it is essentially a more straightforward process that is also easier to achieve comparatively (Dubouis & Grimaud, 2019).

Additionally, during the OER, various intermediates containing oxygen form on the catalyst and get absorbed onto its surface. The formation of these intermediates is a critical step in the process, as this is the only way catalysts can facilitate the breaking and formation of oxygen molecules, which are the intended product. However, this process is challenging to balance as too much binding force will slow down the overall kinetics of the reaction, and the reaction could become “stuck” (J. Li, 2022). This is not a problem for HER because hydrogen atoms and their intermediates are much smaller, easier to release from the catalyst, and involve fewer steps to convert to their molecular form of H2 (Dubouis & Grimaud, 2019).

d-band Theory and Catalyst Performance Prediction for OER

The d-band theory is a fundamental concept used to explain and predict the performance of a transition metal catalyst in a reaction, where it applies specifically to transition metals because of the filled d-orbitals (Bhattacharjee et al., 2016). The theory mainly revolves around the d-band center, which is the average energy level of electrons in the d orbital relative to the Fermi level and is the highest possible energy for electrons at absolute zero, which serves a crucial role in catalytic activity (Bhattacharjee et al., 2016).

The theory’s predictions depend on the relative position of the d-band center (Bhattacharjee et al., 2016). Regarding OER specifically, the theory can predict how well a catalyst binds with oxygen-containing intermediates such as OH, O2, and OOH. When the d- band center is closer to the Fermi level, the interaction between the catalyst and the reaction intermediates generally increases. Conversely, a larger proximity will weaken these interactions (Bhattacharjee et al., 2016). This does not mean that aiming for the highest d-band center (i.e., closest proximity) will be the best. As explained above, a too-strong interaction will hinder oxygen molecules’ release (desorption) and will have a lower efficiency overall. Using the same logic, if the d-band center is too low, the intermediates will not bind strongly, leading to a higher activation energy requirement for the reaction (Bhattacharjee et al., 2016). As a result, catalysts with an optimal d-band center are defined as those that can strike a good balance between adsorption and desorption, allowing for an efficient intermediate process without energy losses, meaning higher overall efficiency. This is because the optimal balance of the d-band center can minimize overpotentials, which will enhance catalytic performance by wasting less energy, leading to a higher energy efficiency, thus allowing more energy to be applied to the reaction itself, increasing the reaction rate (Bhattacharjee et al., 2016). Additionally, over-potential has side effects like releasing heat and increasing material/catalyst stress, which can lead to a shortened lifetime and be suboptimal for future industrial operations. On the other hand, a lower overpotential will help maintain the stability of the catalyst, reducing wear and tear and, as a result, prolonging its lifespan, ultimately leading to the development of cheap and practical catalysts (Dubouis & Grimaud, 2019).

Application of d-band Theory in Catalyst Manufacturing and Design

The d-band theory can be applied in catalyst designs and manufacturing, with it serving as the guiding principle for tailoring catalysts by adjusting the electronic structure via alloying or doping various materials (Chen & Zhang, 2022). The theory has been validated through Density Functional Theory (DFT) calculations, a standard tool for validating and studying catalysts, supporting the effectiveness of the d-band theory (Nørskov et al., 2011). As a result, the d-band Theory can be used as a foundational principle for designing more advanced catalysts that work best for water electrolysis because the d-band theory can enable researchers to have something tangible that they can change for different results (Chen & Zhang, 2022).

Limitations of the d-band Theory for Catalyst Design

Nevertheless, the d-band theory has downsides as it does not apply universally to all catalyst materials (Bhattacharjee et al., 2016). As different catalysts have different electronic structures and surface morphologies, these factors lead to different catalysts requiring a different descriptor that considers and optimizes these factors (B. Wang & Zhang, 2022). For instance, the d-band theory works because it is based on the d orbital electrons, which can play a significant role in reaction intermediates (Bhattacharjee et al., 2016). However, the theory cannot accurately predict the catalytic behavior for materials like metal oxides, on-transition metals, and complex systems, such as perovskites and platinum, because the materials’ structures are too complex to be oversimplified by the d-band behavior in d-band theory (Gorzkowski & Lewera, 2015; B. Wang & Zhang, 2022).

Brief Explanation of HER Mechanism

Moving on to the hydrogen side of the reaction. The HER is a crucial aspect of water electrolysis and the source of green hydrogen. However, despite being generally more straightforward regarding reaction mechanism, HER does not have a definitive or universal theoretical model to predict catalyst performance, unlike OER, which has the d-band theory (Zheng et al., 2018). This makes identifying the optimal catalyst for the reaction completely different and requires understanding and analyzing unique activity descriptors that are not universally applicable to HER. Thus, an overview of HER catalyst design theories is presented below.

Cation Distribution and Spinel Theory for HER Catalyst Design

For HER, a very promising approach in catalyst design is using Spinels. Spinels are a type of crystalline material with the general formula of AB2O4, where “A” and “B” represent different metal cations, and “O” represents oxygen (Elkholy et al., 2017). Spinels generally have a cubic crystal structure characterized by two potential types of sites where the cation can be situated: the Tetrahedral or A site and the Octahedral or B site (Elkholy et al., 2017). These materials are known for their robustness, high thermal stability, and electrical conductivity, making them ideal for industrial applications after an optimal catalyst based on Spinels is successfully developed (Elkholy et al., 2017). One example of spinel is CoFe2O4. In this case, the Co2+ ion occupies the tetrahedral (A) sites while the Fe3+ ion occupies the octahedral (B) sites, together with the four oxygen atoms forming the framework of the molecule (Gomaa et al., 2024).

As there are two sites where cations can reside, a balance needs to be reached between these cations, denoted by δ. The balance significantly affects the catalytic activity for HER, and research has demonstrated that catalysts with an optimal cation distribution can substantially improve catalytic performance. This is because there is a better electron transfer process for the reaction and a more optimized binding strength of the hydrogen intermediates similar to hydrogen (Gomaa et al., 2024). For instance, the spinel of CoFe2O4 is an optimal catalyst for HER. CoFe2O4 has a cation distribution of δ of 0.33, and further research shows that CoFe2O4 exhibits low overpotentials, as low as 66 mV, which is advantageous as low levels of overpotential generally translate to a higher reaction efficiency (Gomaa et al., 2024; Niu et al., 2020). The arrangement of cations in the sites will influence the electronic structure of the spinel, similar to the d-band theory but with much more complicated mechanics (Gomaa et al., 2024). This change in electronic structure will optimize the interaction with hydrogen intermediates, reaching the right balance of binding strength (Exner, 2022).

Hydrogen Adsorption and Desorption Energy for HER Catalyst Analysis and Design

In addition to cation distribution, hydrogen and hydroxyl ions (OH-) adsorption energy has a crucial role in HER, which is especially important in alkaline media with a higher concentration of hydroxyl ions. In a study conducted by Baghban and colleagues, they used DFT to calculate the adsorption energies and achieved a 96.7% accuracy on predicting the behavior of actual catalysts (2021). An ideal catalyst for HER will exhibit a Gibbs free energy close to zero for hydrogen adsorption, meaning it will need less and less energy for the reaction to happen, or, in other words, a lower activation energy given that the catalyst can also efficiently adsorb and dissociate water to provide hydrogen for the reaction (Hu et al., 2016).

Outlook for HER Catalyst Descriptor

Due to the unique nature of the HER, it is essential to consider the current outlook for catalyst design. There are promising developments for HER catalyst descriptor analysis, but a universal and consistently working descriptor theory for HER still does not exist (Dubouis & Grimaud, 2019). Unlike in the case of OER, HER cannot use d-band theory because of complications, and other theories suffer from the same problem, causing the lack of a consistently working universal descriptor theory for HER. The HER involves diverse electronic properties observed in materials available for HER, making it very challenging to establish a single definitive set of rules or descriptors that can apply universally (Du et al., 2025). Admittedly, the d-band theory cannot predict catalysts under all circumstances as previously established, but it is still a valuable OER catalyst analysis approach, which is “better” than the current HER situation. As such, future HER research should focus on developing a more comprehensive theory, allowing the community to progress towards a more comprehensive theory while enabling other potential research areas.

Conclusion

As established previously, energy is critical for society, so developing a clean energy source is also essential. However, current energy generation options have significant limitations, such as pollution or scalability. Specifically, despite being cheap and efficient, fossil fuels are very polluting, while on the other hand, despite being clean, solar power and other renewables are less efficient, intermittent, and consume large amounts of resources to create a working system (Rashad & Hammad, 2000). An alternative to all these methods exists: using hydrogen as a clean fuel source. Hydrogen is an excellent alternative to fossil fuel, as it has a high energy density and low emissions (Hossain Bhuiyan & Siddique, 2025). In addition to that, hydrogen also matters in other fields, as it is an essential industrial resource. The side product generated by green hydrogen production, oxygen, also has an essential industrial application, making green hydrogen production even more tempting (Eckl et al., 2025; U.S. Energy Information Agency, 2024).

Despite these benefits, hydrogen production is mainly achieved using fossil fuel (steam reforming), where 62% of all hydrogen production relies on natural gas (steam reforming), and around 99% of all hydrogen production requires fossil fuel and leads to carbon emissions (International Energy Agency, 2024). Therefore, when hydrogen is used in a hydrogen-based powerplant or a hydrogen fuel cell, it will likely have a carbon footprint comparable to that of normal fossil fuel. As such, developing a completely carbon-neutral method to produce hydrogen, specifically electrolysis, is essential. Nevertheless, electrolysis has problems because it is inefficient and not easily scalable, especially for industrial operations. To solve this issue, catalysts that can meet the requirements of an industrial system can be used to make the reaction less energy-consuming, hence allowing us to achieve efficient large-scale water electrolysis. This requires a theory that can reliably identify the respective activity descriptors for both the HER and OER. As discussed throughout the paper, the catalyst for the OER can be predicted using the d-band theory by optimizing the adsorption and desorption of oxygen-containing intermediates, whereas HER performance depends on more material-specific approaches and is generally harder to define. However, methods to identify the optimal HER catalysts exist, including understanding cation distribution in spinel systems that use catalysts of a specific format, such as CoFe2O4. These strategies demonstrate that by understanding electronic and atomic structures, specifically the d-band center in transition metal-based catalysts and spinel cation balance, the performance of catalysts for water electrolysis can be effectively and quantitatively predicted. In conclusion, optimizing catalyst selection and advancing in d-band and spinel theory or other potential theories are necessary for the bigger goal of large-scale clean hydrogen production. Therefore, more focus, funding, and research should be directed towards understanding and developing these catalysts to enable their industrial use, from energy production to industrial uses, not only laboratory applications.

This paper provides a review of the current available methods in identifying the activity descriptors for both the HER and OER and does not aim to find new methods or theories. To solve the problem of catalyst design, more experimental trials and data on catalyst designs need to be done, which will enable the potential for further understanding of catalysis or even potentially finding the catalyst that can be applied in the industry. Also, this review does not include all aspects of the research, as the paper only discussed the theories related to water electrolysis and their associated catalysts, which themselves can still benefit from catalysis research developments in other fields. Specifically, catalysis research has been done in fields other than water electrolysis catalysis, and we anticipate that future work could incorporate findings from those fields into the field of water electrolysis. Doing so can compile a more comprehensive and effective review, providing more value to the field.

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About the author

Jiajun Li

Jiajun is currently a 12th grade student at St. Andrew’s College. He is interested in physics, specifically nuclear physics, as well as environmental science, specifically the energy aspect. Jiajun is currently investigating how a clean and renewable energy source can solve most of the environmental crises that we are currently facing and how to develop future energy sources, such as advanced fission reactors and nuclear fusion reactors, which could greatly benefit society.

Jiajun is the leader and founder of his school’s physics club and a vital member of the environmental council, which has made significant progress on helping the environment within his school, including reducing food waste by over 20%. At his previous school, three other students and Jiajun succeeded in installing a solar energy system, and he is also planning the installation of a larger solar power system to power his current school.

The post Engineering Catalysts for Water Electrolysis: A Review of Activity Descriptors for Hydrogen and Oxygen Evolution Reaction appeared first on Exploratio Journal.

Abstract

This study examined the effect of glucose concentration on carbon dioxide (CO₂) production during yeast fermentation to determine the optimal sugar level for maximizing anaerobic respiration. Six trials were conducted using 500 mL bottles containing 200 mL of water at 30°C, 7 g of dried yeast, and varying glucose amounts (0–25 g). CO₂ output was indirectly measured by recording the circumference of balloons attached to each bottle at 10-minute intervals over 50 minutes. Results showed that moderate glucose concentrations (10–15 g) produced the greatest CO₂ output, with the 10 g trial yielding the highest balloon circumference increase (21.4 cm). Lower (0–5 g) and higher (20–25 g) glucose concentrations resulted in reduced CO₂ production, potentially due to substrate limitation or ethanol inhibition. The findings suggest a threshold beyond which additional glucose may hinder fermentation efficiency. Despite limitations—including a lack of repeated trials and balloon leakage—this experiment highlights the importance of glucose optimization in fermentation-based industries.

Purpose

How does the amount of sugar affect the amount of CO₂ released in a bottle with water, sugar, and yeast in a span of 50 minutes?

Background information

In order for cells to receive the necessary quantity of energy for functioning, in the absence of oxygen (O₂), there must be an anaerobic respiration process classified as “fermentation”, in which sugars, such as glucose and fructose (C₆H₁₂O₆) produce changes in organic substrates in order to provide energy (ATP) for cells. “Glycolysis is a cytoplasmic pathway which breaks down glucose into two three-carbon compounds and generates energy” (Kumari, 2018). This indicates that glycolysis is the initial stage of converting glucose into energy for cellular metabolism. This must occur prior to fermentation, since without the breakage of sugars by cells into two smaller sugar molecules( of a chemical called pyruvate) in enzyme reactions, ATP wouldn’t be produced since the splitting of the cells is what initially produces energy. Fermentation has been a crucial part of science for human evolution since it has allowed the formation of edible elements such as yogurt, wine, beer, miso, kimchi, and bread. All of these processes have been performed because of the usage of yeast (C19H14O2 – bakers yeast [most common type of yeast]) combined with sugars, which later create ethanol (C₂H₆O – also known as alcohol) and carbon dioxide (CO₂). “An important feature of yeasts (especially Saccharomyces cerevisiae – a.k.a. S. cerevisiae) is that their cells […] are eukaryotic – meaning they have a nucleus containing DNA packaged into chromosomes. And although it may seem like yeast and humans have very little in common, at least 20 percent of human genes known to have a role in disease have counterparts in yeast.” (FenoLogica, 2017). This indicates that the study of yeast allows the improvements of medical analysis and advances because of the similarity within yeast and human cells. The investigation of yeast and fermentation is substantial since finding a more rapid fermentation procedure can increase the production ofthe yeast-based products mentioned previously, which could positively impact the global economy, raising the markets of yeast-made products.

This specific investigation focuses on how the quantity of sugar (C₆H₁₂O₆) additioned to a mixture of yeast and water on a 500mL bottle impacts the CO₂ production of fermentation. The independent variable chosen was the sugar concentration in the experiment. This is because sugars are the main stimuli for fermentation to occur, and incrementing/decreasing the sugar concentration added per experiment trial may impact the speed of production of CO₂molecules. The dependent variable in this experiment is the rate of CO₂ production and/or the circumference of the balloon. This is because the production of CO₂ will be dependent on the amount of sugar (in this experiment) added to the yeast/water mixture since the chemical element that is released after fermentation is CO₂ . The quantities of sugar added per trial (bottle) were calculated in a 5-by-5 matter, beginning at 5g (including control variable: 0g), and ending at 25g, having 6 trials overall (including control). This was chosen as an effective measure of sugar quantities so that there wouldn’t be too little nor too much sugar in each trial. “In the 1850s and 1860s, the French chemist and microbiologist Louis Pasteur became the first scientist to study fermentation, when he demonstrated that this process was performed by living cells. Fermentation processes to produce wines, beers and ciders are traditionally carried out with Saccharomyces cerevisiae strains, the most common and commercially available yeast.” (Maicas, 2020). According to the previous passage, the study of fermentation had been intentional for the past 173-163 years, and indicates that fermentation can be produced with traditionally-known yeast. This means that we should continue to investigate it since “genetic manipulation in yeast is easy and cheap compared to similar experiments in more complex animals such as mice and zebrafish” (YourGenome, 2021). With yeast, one can do complex experiments regarding various topics in an easy, efficient way.

C6H12O6 (aq) ————> 2C2H5OH (aq) + 2CO2(g) + 2ATP

Hypothesis

If glucose levels impact the quantity of CO₂ production during fermentation, the more glucose (sugars) added to a 30* celsius water-yeast experiment, the higher the anaerobic respiration rate, the more CO₂ produced.

Variables

Independent variable (Manipulated variable):
Concentration of Sugar

Dependent variable (Measured variable):
Balloon Size (rate of CO₂ production)

Control variables:
Amount of water (200mL), amount of yeast (7g), size of bottles, temperature of water, temperature of the environment tested in, time of the day tested in , size + type of the balloons, same measuring tools per experiment.

Materials

• Dried yeast (total of 42 grams)
• 6 empty 500mL water bottles
• Granulated sugar (total of 75 grams)
• 6 Same size balloons
• Graduated cylinder
• Water
• Funnel
• String
• Ruler
• Tape
• Marker Pen
• Timer
• Thermometer
• Weightboat
• Mass
• A notepad and pen

Procedure

• 1. Place all materials listed on a stable surface
• 2. Place short strips of tape on each bottle
• 3. With a marker, label bottles as corresponding Bottle A = 5g, Bottle B = 10g, Bottle C = 15g, Bottle D = 20g, Bottle E = 25g and Control = 0g
• 4. Take the temperature of the water available for testing.
• 5. Measure 200mL of water using the graduated cylinder
• 6. Pour the water onto a bottle using a funnel
• 7. Repeat steps 4-5 until all bottles contain 200mL of water
• 8. Weight 5g of sugar with the weightboat
• 9. Add the 5g of sugar to Bottle A
• 10. Weight 10g of sugar using the weightboat
• 11. Add the 10g of sugar to Bottle B
• 12. Weight 15g of sugar using the weightboat
• 13. Add the 15g of sugar to Bottle C
• 14. Weight 20g of sugar using the weightboat
• 15. Add the 20g of sugar to Bottle D
• 16. Weight 25g of sugar using the weightboat
• 17. Add the 25g of sugar to Bottle E
• 18. Weight 7 grams of dried yeast on the mass/scale with the weightboat
• 19. Add the 7 grams of yeast to a bottle using the funnel
• 20. Repeat steps 18-19 until all bottles have 7g of yeast each
• 21. Attach one balloon on the open top of each bottle to close it
• 22. Gently raise each bottle and shake it for at least 15 seconds – place bottles down after shaking is done
• 23. Annotate observations plus circumference (size) of all balloons before initiating trials in a notepad24. Start a timer for 50 minutes utilizing a cell phone/computer
• 25. After every 10 minutes (until 50 minutes), use a string and a ruler to measure the circumference of
• each water bottle-balloon attachment and annotate in a notepad (place string around balloon and
• measure string’s length with the ruler)
• 26. Write down observations of every water bottle-balloon attachment on the notepad
• 27. End the experiment after 50 minutes
• 28. Rewrite the data + observations placed in the notepad onto the data table
• 29. When finishing experiment, discard ingredients and clean up the lab area

Data and Observations

Circumference of Balloons Depending on Sugar Amount

All the bottle’s liquid turned foggy and foam was created.

Conclusion

The purpose of this experiment was to determine if the concentration of sugar added to a 200mL 30*celsius water and 7g yeast solution (inside a bottle) impacted the rate of CO₂ production in fermentation during a span of 50 minutes. There was one trial held for this experiment, with 5 testing elements. In the 5 testings done with 5 different 750ml bottles, all containing the water-yeast mixture, Bottle A, contained 5g of sugar, Bottle B, 10g, Bottle C, 15g, Bottle D, 20g, and finally Bottle E contained 25g of sugar. To measure the amount of CO₂ produced per bottle, a balloon was placed and secured on the opening of every bottle, and the circumference was measured every 10 minutes (until reaching 50) with the utilization of a string and a ruler. For all bottle trials, the initial balloon circumference was 11cm. The balloon circumference that after 50 minutes grew the most was Bottle B, containing 10 grams of sugar. This balloon grew until it reached a 32.4 cm circumference, growing a total of 21.4 centimeters. On the other hand, the balloon with the least growth was Bottle E, containing no sugar at all. This balloon grew 0.2 cm, reaching a circumference of 11.2cm total. The order of balloons with the biggest circumference according to the data table and figure 4, from least to greatest were: 0g of sugar (11.2 circumference – 0.2cm growth – 0.004 cm/min rate of change), 5g of sugar (14.6 circumference – 3.6cm growth- 0.072 cm/min rate of change), 25g of sugar (28.2cm circumference – 17.2cm growth – 0.344 cm/min rate of change), 20g of sugar (28.5cm circumference, 17.5cm growth- 0.35 cm/min rate of change), 15g of sugar (32.1cm circumference – 21.1cm growth – 0.422 cm/min rate of change) and 10g of sugar (32.4 circumference – 21.4cm growth- 0.428 cm/min rate of change). This study rejects the original hypothesis, which claimed that as sugar was added, CO₂ production increased, increasing the balloon’s circumference. As shown in the data above, bottles with little sugar (0–5g) had a slower rate of CO₂ production. Unexpectedly, the bottles with high sugar content (20–25g) had a higher rate of CO₂ production than those with low sugar content, but they didn’t reach the same levels of CO₂ production as the bottles with 10-15g of sugar. This is because “ too much sugar can result in yeast that becomes stressed out and gets overwhelmed by ethanol production, potentially creating too much alcohol, which will kill off the yeast, also halting the fermentation process.”(Oculyze, 2022). This suggests that the first step in fermentation, glycolysis, cannot take place as intended because there are too many sugar molecules (C6H12O6) breaking down and producing large amounts of ethanol (C2H6O), which interfere with the ability of the yeast to break down sugar molecules and produce carbon dioxide (O₂) since ethanol becomes the dominant element in the mixture. Although any amount of sugars (glucose, fructose) will cause fermentation to occur, a moderate amount of sugar, about a ratio of 10-15 grams per every 200 ml of water and 7g of yeast, will produce more carbon dioxide according to the experiment. With this process, C₆H₁₂O₆ and C19H14O2 were transformed into C₂H₆O and CO₂.

Evaluation

In this investigation, there was a not-sufficient data collection, leading to an inconclusive claim. There should have been at least 4-5 trials in order to obtain a solid conclusion, yet there was only one with multiple inconveniences and errors. As the sugar was being massed (to reach 10 grams), water fell into the sugar which is believed to be a possible cause of the high and accelerated CO₂ production since a sugar-water mixture was activated prior to being added to the yeast-water solution. Additionally, two leaks were found. The first leak was found at 37.8 minutes in the bottle containing 20 grams of sugar, which was later patched with tape. The second one was found at 50 minutes in the bottle with 25 grams of sugar, during the laboratory clean up. The conclusion stated in this experiment was that there has to be a moderate ratio of sugar (in the experiment’s specific case, 10-15g) in order for the sugar to activate yeast to the maximum in order for more CO₂ to be produced since when too little sugar was added to the solution, there was a small growth, while when there was a lot of sugar added, the balloons grew, yet not as much as those with considerably moderate amounts of sugar. This scientific mechanism, called saturation, usually occurs with larger amounts of sugar in bigger experiments, so it happening with the 200ml water and 7g yeast mixture is unusual. Thus, the results being this way could be because of the leaks found on the balloons, since the 20g of sugar balloon, after being patched, grew rapidly. The leakage led to many variations in the data collected, which influenced the conclusion and final results. Many of the variables tested were not controlled such as the temperature of the environment, elements such as air conditioning being on, and the surface tested at. These variables were environmental, meaning lab workers had no control over them.

To have more accurate results, the data of CO₂ production should have been collected using a CO₂ probe, and the experiment should have been done in a more careful way, checking for possible leaks every 5-10 minutes.

Discussion

This investigation and any yeast investigation is crucial for the global economy since yeast, as mentioned while introducing the experiment, is necessary for the production of beverages and edibles like bread, miso, kimchi, and beer which are common and very sold in supermarkets. With this investigation, the producers of these elements could benefit from knowing when the sugars activate the yeast in these elements, since it would cause a more rapid production of them. One way to make the results in this experiment more reliable, guaranteeing a more solid base of information for yeast-involved edible producers would be to increment the quantities of sugar and testing with more bottles (20 approximately) which would allow producers to know when saturation occurs in order to prevent adding too much sugar to the ratio of other elements such as yeast and water previously established. A second investigation that could be beneficial would be having the same experiment with larger quantities and concentrations of water, sugar, and yeast with larger containers for testing (plus more trials). Large industries usually utilize large amounts of materials for production, so testing with more quantities would provide a better and more representative insight on how the quantities of sugar impact CO₂ production from a yeast-activated solution in a factory-environment.

References

Glycolysis – an overview | ScienceDirect Topics. (n.d.). Retrieved March 12, 2023, from www.sciencedirect.com/topics/neuroscience/glycolysis

Maicas, S. (2020). The Role of Yeasts in Fermentation Processes. In PubMed Central (PMC). Multidisciplinary Digital Publishing Institute (MDPI). www.ncbi.nlm.nih.gov/pmc/articles/PMC7466055/

Tarziu, C. (n.d.). How Does Ethanol Affect Yeast Fermentation? In Oculyze. Retrieved March 22, 2023, from www.oculyze.net/how-does-ethanol-affect-yeast-fermentation/

Why use yeast in research? (n.d.). In @yourgenome · Science website. Retrieved March 13, 2023, from www.yourgenome.org/facts/why-use-yeast-in-research/

Why Yeast is Important to Scientific Discovery — . (2017, August 29). www.fenologica.com/

. 

About the author

Maggie Zaidman

Maggie is a dedicated student with a strong interest in neuroscience, biology, and medicine. Maggie’s research spans Alzheimer’s disease, synthetic biology, and aplastic anemia, and she is a 2024 Yale Young Global Scholars alumna.

As the founder of the organization EmpoderadaMente, Maggie empowers women in Perú by making neuroscience accessible. Through her initiative, she authored Conoce tu Mente, Mejora tu Vida, a book that explains neuroplasticity and brain health in practical terms. Maggie also collaborated in the iGEM FDR-HB team, where she played a key role in a synthetic biology project that engineered E. coli to degrade PET plastic and convert it into ethanol, aiming to create a sustainable energy solution for low-income communities.

Maggie aspires to bridge the gap between scientific research and patient-centered care, using her skills to drive positive change in both healthcare and community outreach.

The post Effect of Glucose Concentration on CO₂ Output in a Yeast Fermentation Experiment appeared first on Exploratio Journal.

Abstract

The purpose of this research is to compare the two popular marine microplastic sampling methods: Manta net method and the Neuston net method. Specifically, the precision in the data that each method collected is compared, and a conclusion is deduced. This will be done using various viewpoints including a histogram, a world map microplastic density diagram, and a box-and-whisker plot consisting of count of density measurements vs. density measurements, pure density measurements, and time vs. density measurements respectively. The comparison of precision is done for each viewpoints, and the method that has a higher precision in the data it collected is concluded in the conclusion section.

1. Introduction

Since the worldwide commercialization of plastic products, microplastic particles have been mixed into our drinking water. These microplastic particles are small plastic particles which their diameters are less or equal to 5 millimetres. These microplastic particles can possibly be hazardous to human body when consumed, although there are some debates about the specific effects, including lipid metabolism, induce oxidative stress, and include neurotoxic responses. In order to avoid these harmful effects, there is a need to filter out the microplastic particles from our drinking water. 

However, as microplastic particles are distributed around the globe differently by region, there is a need to use different filtering plans depending on the distribution. And in order to formulate specific plans by region, there is a need to apprehend the specific densities in marine environments by region. Since it is realistically impossible to observe microplastic particles in every ocean, samples have to be used to determine the approximate densities of microplastic particles in the oceans. 

Two useful methods for sampling of microplastic densities are using a Manta net and using a Neuston net. These two sampling methods have different properties, and the selection of which method is going to be used should be determined by evaluating these two methods critically, and concluding which method has more reliability in the measurements. This paper will compare the data collected by using these two sampling methods, and determine which sampling method has higher precision in their measurements.

1.1 Background Research

1.1.1 Manta Net Method

The Manta Net’s name comes from Manta Rays, a sea animal that feeds from small sea organisms at the ocean’s surface. As this name indicates, the Manta Net’s original function was to collect small sea organisms from the surface of the ocean. But as the Manta Net is capable of collecting micro-sized objects, it was started to be used as a microplastic density sampling tool from oceans. 

The specifics of the properties of the Manta Net’s structure are the following:

• The opening of the net has varying dimensions, the width varying from 30 cm to 120 cm, and the height varying from 10 cm to 75 cm. The most common values for these dimensions are 60 cm for width and 15 cm for height according to various researches. 
• Following the dimensions of the opening, the net’s length varies from 200 cm to 300 cm
• The mesh size the of the manta net vary from 300 μm to 350 μm, and the most common mesh size is 330 μm. 

1.1.2 Neuston Net Method

The Neuston net is named after the species “Neustons,” aquatic organisms that stays mostly on the surface of water that originates from planktons. As can be known for the origin of the name, Neuston nets are used for surface sampling of water surface organisms such as zooplankton, but it also used to sample marine microplastic on the surface of water used for research. 

The specifics of the properties of the Neuston nets vary significantly by its function, usage, and design, but the most common properties are the following:

• The opening of the net has varying dimensions, the opening area varying from 0.5 to 1 square meters 
• Following the dimensions of the opening, the net’s length varies from 3 to 8 meters.
• The mesh size the of the neuston nets are usually 333 μm and 335 μm, 

1.1.3 Properties of microplastics in the ocean

A crucial piece information that is essential for the filteration of microplastic is their properties. Microplastic particles are plastic pieces that consists of dimensions shorter than 5 mm, which are part of beads, fragments, pellets, film, foam, and fiber. These microplastic particles are made from different types of polymer chains, the most abundant and dominant type of polymers being polyethylene and polypropylene. 

Polyethylene microplastic particles come from plastic bottles, water tanks, and bags. A reason for these applications is because of its hydrophobic property. As the molecular structure of the polyethylene is non-polar, water, or other liquid with polar structures, is repelled and as a result is water-proof. And as polyethylene has relatively low density, it tends to float on the surface of water, which can be sampled using Manta net or Neuston net.

Polypropylene microplastic particles come from food packaging, automobiles, and electronics. Polypropylene is water-proof, as its water absorption rate is 0.01% after 24 hours in water. Also, polypropylene has a very low density, which makes it capable of being sampled by Manta net or Neuston net.

1.2 Data and Method

1.2.1 Data Base used for Data Collection

The data base used in this research is from “National Centers for Environmental Information” (NCEI), which is based on various sampling methods of microplastic particles in the ocean surface. The sampling methods that are going to be examined are Manta net method and the Neuston net method. 

The data base consists of the date the data was collected, the Latitude of the collection position, the longitude of the collection position, the ocean that the data was collected, region of collection (including subregions), measurement of the density of the particles (pieces/m³), density class range, concentration class, sampling method, references, organization of collection, and accession numbers. The database can be accessed through an article published by the NCEI which has a title “Marine Microplastics,” which will be cited below in the Work Cited section (section 4).

1.2.2 Software used for Data Analysis

In this research, the Tableau Public 2024.2 software was used to analyze and organize the data. Using this software, the pieces of data in the data base was organized into different diagrams in order to explore the different aspects of the effectiveness of the Manta net and the Neuston net method in the sampling of microplastic in marine environments.

2. Research Question

Between the Manta net sampling method and Neuston net sampling method, which method has more precision in sampling microplastic particles in marine environments?

3. Data Collection and Analysis

3.1 Measurement vs. Count of Measurement

The histogram shows the count of measurements for each measurement values according to the two sampling methods, Manta Net method and Neuston Net method represented as blue and orange respectfully. The range of values for the count of measurements are set to be larger or equal to 1, zeros in the y axis will create many holes in the histogram, which will make the data be difficult to compare. Also, for the difference to be shown clearly shown in the model, the upper maximum value for the measurement values has been set to 0.1, since the values of the count of measurement for the two models above the measurement values of 0.1 were consistently 1, which is insignificant for the comparison of the data to conclude which sampling method has higher precision than another. 

This histogram has several implications, first showing the significant difference in count of measurements by sampling method. The highest count of measurement for all measurements is 6 for Manta Net method, and 536 to the third significant figures for the Neuston Net method. The big difference in the number of values gives a portion of the indication that the Neuston Net method has a higher precision than the Neuston Net method, as the higher number of the count of measurement may show the measurements not varying in a large amount throughout repeated trials. However, the distribution of the measurements, specifically the range of the count of measurements has to be analyzed in order to conclude that the Manta Net method has a higher precision than the Neuston Net method.

The range of the count of measurement for the two marine microplastic sampling methods is a strong indication of their precision, as a larger range indicates that the values measured were more consistent throughout the measurement process, especially for the measurement value that the maximum value for the count of measurements is associated with. The range values can be deduced by the following:

As the count of measurement values for the histogram is set to have values of larger or equal to 1, the lowest count of measurement that is shown in the histogram is 1. The Manta Net method’s range of the count of measurement values can be calculated by subtracting the minimum value from the maximum, which gives 6-1=5. Therefore, the Manta Net method’s range of the count of measurement is 5. The Neuston Net method’s range of the count of measurement values can be calculated by subtracting the minimum value from the maximum, which gives 536-1=535. Therefore, the Neuston Net method’s range of the count of measurement is 535. In terms of the whole distribution of values, the range of 5 for the Manta Net method and the range of 535 for the Neuston Net method is a significant difference. And as the Neuston Net method has a larger range than the Manta Net method, it can be deduced that the Neuston Net method had consistent measurements, specifically for the measurement value of 0.00216 which the 536 count of measurements is associated with, and therefore it can be pre-concluded that the Neuston Net method has higher precision than the Manta Net method in terms of sampling marine microplastic densities.

However, there are limitations in this model. First, the region that these measurements were taken are not considered in the histogram. As precision is defined as the consistency of the measurements of microplastic densities in water within a certain region, the region that the values were taken from has to be analyzed in order to accurately conclude which sampling method has higher precision. For the conclusion made above using the histogram, the region that the measurement value of 0.00216 was measured has to be considered, and if they are taken from different regions, the conclusion will vary according to the portion of values that were taken from the different regions. Secondly, the amount of time that each sampling method used to collect the measurement values are not considered. Longer time taken for the collection of the data may result in larger counts of measurements, which can contaminate the data. The time taken for the measurements should also be taken into account in order to make an accurate conclusion for the precision of the two sampling methods.

3.2 World Map Microplastic Density Diagram Analysis

The world microplastic density diagrams show the density measurements for each measurement values according to the two sampling methods, Manta Net method and Neuston Net method represented as blue and orange respectfully. The range of values for the measurements are set to be larger or equal to 0.0007 pieces/m³, as the density is measured in this case so the values can be measured lower than 1, but has to be higher than 0. The lowest value found in the set was 0.0007 pieces/m³, which is applied to the filter in the software. Also, for the difference to be shown clearly shown in the model, the upper maximum value for the measurement values has been set to 1 pieces/m³, since the values above 1 pieces/m³ are not many in count but much higher in value, which makes the majority of the values not significantly seen as visuals as this diagram’s features higher density values as larger area of the color in respect to the sampling methods. The data base as a whole has most of the measurements done in the Pacific and Atlantic Ocean, and the other oceans’ measurements has insignificant difference between the two sampling methods. Therefore, the other regions will be neglected from the discussion for the reason of concision and significance. 

As this world map diagram shows the microplastic density in terms of regions, it can be used to evaluate the consistency of data collection in specific regions. First, to examine the Pacific Ocean region, there is not a very notable difference in the orange and blue regions shown in Figure 2 and Figure 3 respectively. However, there is a difference in area of the colored region. Figure 2 shows a variety of size of area of the colored region, consisting of many smaller areas and relatively fewer larger areas. Conversely, Figure 3 shows relatively higher consistency in the size of blue colored areas, mostly larger colored areas, also in a more compacted region in the Pacific Ocean. Therefore, it can be pre-concluded that in the Pacific Ocean region, the Manta net method has a higher precision than the Neuston net Method, as it has less random errors in the measurement in the same region.

The Atlantic Ocean region has a very significance in the measurement regions between the Manta net and Neuston net sampling methods. In figure 2, the Neuston net method shows a relatively consistent measurement in a compacted area near North America and Central America, while almost no measurement was done with the Manta net method in Figure 3. In this region, the Neuston net method shows high precision as the size of the colored areas are relatively consistent, and the region of measurement is compacted. In the European region of the Atlantic Ocean, there are relatively more measurements done using Manta net method than the Neuston net Method. In this region, the measurements made using the Manta net method has a variety of size of the colored region, having more small colored areas compared to the Neuston net method’s sampling near the North and Central America. Also, the region the measurements were made is relatively larger, which also impacts the amount of data collected by decreasing the consistency of the measured regions. Therefore, in the Atlantic Ocean region, it can be pre-concluded that the Neuston net method has higher precision than the Manta net method in marine microplastic sampling.

However, there are limitations in this model. First, the total amount of measurements done for each method was not taken account. The number of trials is crucial to deducing the precision of a sampling method, as difference in the number of trials can impact the data set to have higher or lower precision than the other method. Therefore, the conclusions made were under the assumption that the number of trials of the sampling using Manta net and the Neuston net does not have a large difference, which will allow a conclusion to be deduced from this diagram. Also, the duration of the data collection has not been considered in this diagram. If the duration of the measurement is very long, the measurement of the microplastic density for both sampling method is capable of varying as time passes, which will impact the precision for both methods as the values themselves change during the trials. Therefore, the conclusion is under the assumption that the values changed not in a large amount between trials.

3.3 Box-and-Whisker Diagram by Time vs. Measurement

The box-and-whisker diagrams show the density measurements for each measurement values according to the two sampling methods, Manta Net method and Neuston, both according to the year that the data was collected. The range of values for the count of measurements are set to be larger or equal to 0.0007 pieces/m³, as the density is measured in this case so the values can be measured lower than 1, but has to be higher than 0. The lowest value found in the set was 0.0007 pieces/m³, which is applied to the filter in the software. Also, for the time of measurement, the years that both methods commonly had been used for the convenience of comparison between the data of two sampling methods. 

To analyze the box-and-whisker plot to compare the precision of the two sampling methods, the number of outliers is the factor. As precision is defined as the consistency of measuring the values between trials of the same condition, in this case, the year that the measurement was done, the higher number of outliers shows that the method has a low precision. In order to calculate the number of outliers, the upper and lower whiskers of the box-and-whisker diagrams have to be calculated, since the values that are higher or lower respectively are outliers of the data. The calculation for the minimum value, or the lower whisker, is done as: 

Minimum Value = Q1 – 1.5(IQR)

where Q1 is the 25^th percentile of the data and IQR is the interquartile range of the data. For the upper The calculation for the maximum value, or the lower whisker, is done as:  

Minimum Value = Q3 + 1.5(IQR)

where Q3 is the 75^th percentile of the data. The values for these maximum and minimums are calculated for each year for figure 4 and 5, which is shown in the table below:

The count of the number of outliers for each year for the two sampling methods is shown in the table below:

As can be seen in Table 3, the box-and-whisker diagram for the Manta net method (Figure 4) has much less years that has more outliers than the Nueston net method, which shows that the precision for each year is dependent on the consistency of the values measured in each trials in the same condition, which means that the outliers reflect an inconsistency in the measurements. Therefore, in the box-and-whisker diagram aspect in respect to the time the measurement was done, it can be pre-concluded that the Manta net method has a higher precision than the Neuston net method.

4. Conclusion

By examining the three diagrams, it could be known that Manta net method and Neuston net method has higher precision when viewed in certain aspects but lower in another; specifically, the histogram viewpoint shows that the Neuston net method has higher precision than the Manta net method, the world map view shows varying results for different regions of ocean that the measurement took place, and the box-and-whisker diagram shows that the Manta net method has higher precision than the Neuston net method. Therefore, it can be concluded that the two method has similar precision in general. However, as the world map showed more significant results in the Atlantic Ocean region where Neuston net method is shown to be more precise, it can also be concluded that the Neuston net sampling method has higher precision than the Manta net method in sampling marine microplastic. 

Works Cited

Author links open overlay panelGabriel Erni-Cassola a, et al. “Distribution of Plastic Polymer Types in the Marine Environment; a Meta-Analysis.” Journal of Hazardous Materials, Elsevier, 21 Feb. 2019, www.sciencedirect.com/science/article/pii/S0304389419301979. 

 “Marine Microplastics.” National Centers for Environmental Information (NCEI), 18 Nov. 2024, www.ncei.noaa.gov/products/microplastics. 

“Neuston.” Neuston – an Overview | ScienceDirect Topics, www.sciencedirect.com/topics/earth-and-planetary-sciences/neuston. Accessed 26 Nov. 2024. 

Nyadjro, Ebenezer S., et al. “The NOAA NCEI Marine Microplastics Database.” Nature News, Nature Publishing Group, 20 Oct. 2023, www.nature.com/articles/s41597-023-02632-y. 

Pasquier, Gabriel, et al. “Manta Net: The Golden Method for Sampling Surface Water Microplastics in Aquatic Environments.” Frontiers, Frontiers, 21 Feb. 2022, www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.811112/full. 

Sharma, Kabita. “Polyethylene: Structure, Properties, Types, Uses.” Science Info, 3 Apr. 2024, scienceinfo.com/polyethylene-structure-properties-types-uses/. 

Useon. “Polypropylene (PP) Plastic: Types, Uses and Processing.” USEON, 18 Nov. 2024, www.useon.com/polypropylene/. 

US Department of Commerce, National Oceanic and Atmospheric Administration. “NOAA National Ocean Service Education: Coastal Pollution Tutorial.” NOAA’s National Ocean Service, 22 Oct. 2019, oceanservice.noaa.gov/education/tutorial-coastal/marine-debris/md04.html. 

About the author

Namwoo Cho

Namwoo is a grade 12 student at the Shanghai American School. He is a future engineering student, and is interested in physics and chemistry.

The post Between the Manta Net Sampling Method and Neuston Net Sampling Method, Which Has More Precision in Sampling Microplastic Particles in Marine Environments? appeared first on Exploratio Journal.

Abstract

Aerodynamics plays a pivotal role in engineering, particularly in the design and optimization of vehicles and structures that interact with air, such as aircraft, cars, and rockets. The challenge of minimizing drag, maximizing lift, and optimizing thrust has historically relied on computationally expensive simulations, including Computational Fluid Dynamics (CFD). Recently, the integration of machine learning (ML) techniques into aerodynamic optimization has opened new pathways for more efficient and cost-effective design processes. ML models can identify patterns and predict aerodynamic performance with high accuracy, reducing the need for exhaustive simulations. This paper explores the role of machine learning in aerodynamics, with a focus on its applications in aerodynamic shape optimization, drag reduction, and hybrid approaches combining ML with CFD simulations. In addition, we examine how ML is transforming materials science, enabling the discovery and optimization of materials that complement advanced aerodynamic designs. The intersection of these fields offers exciting opportunities for innovation in aerospace engineering, with the potential to improve performance, efficiency, and sustainability in current and future designs.

Keywords: Aerodynamics, Machine Learning, Design Optimization, Computational Fluid Dynamics, Materials Science

1. Introduction

Aerodynamics is a critical field within engineering, influencing the performance and efficiency of vehicles, aircraft, and structures that interact with air. The need for aerodynamic optimization stems from key factors such as reducing drag, enhancing lift, and improving thrust efficiency—elements central to the design of various transport and aerospace systems. Traditionally, aerodynamic design has relied heavily on computational fluid dynamics (CFD) to simulate airflow and predict performance. However, this approach can be computationally expensive and time-consuming, particularly for complex, three-dimensional designs.

Recent advancements in machine learning (ML) present new opportunities for optimizing aerodynamic designs and materials. ML models can process vast amounts of data and identify patterns within complex design spaces, enabling faster and more accurate predictions of aerodynamic behaviour. This paper delves into the role of machine learning in aerodynamics and materials science, emphasizing its impact on design optimization and the development of innovative materials. The integration of these two fields promises to revolutionize aerospace engineering, offering potential solutions to key challenges in performance, sustainability, and cost-efficiency.

2. The Fundamentals of Aerodynamics

2.1 Key Forces in Aerodynamic Engineering

Aerodynamics is the study of how air interacts with objects in motion, playing a vital role in the design of various transportation systems. In aerospace engineering, the primary forces at play are drag, lift, and thrust:

Drag: The resistance encountered by an object as it moves through air. Drag is influenced by the object’s shape, speed, and the air’s density.

Lift: The upward force that counteracts gravity and allows objects like aircraft to remain in flight. It is generated by the differential pressure across the wings (Fig 1)

Thrust: The forward force produced by engines, which propels an object through the air and counteracts drag (Fig 1)

Efficient aerodynamic designs aim to optimize these forces to improve performance, reduce fuel consumption, and ensure stability and safety. Streamlined shapes are often used in vehicle design to minimize drag and maximize efficiency (Fig 1)

2.2 The Role of Aerodynamic Optimization

The objective of aerodynamic optimization is to improve the efficiency and performance of designs. It focuses on adjusting various parameters—such as the shape, size, and surface characteristics of an object—to reduce drag, enhance lift, and improve fuel efficiency (fig 2). Optimization techniques are applied using methods like computational fluid dynamics (CFD), which simulate airflow over different geometries. This iterative process seeks to refine designs, balancing competing goals such as maximizing lift while minimizing drag.

An important challenge in aerodynamic optimization is the high computational cost associated with simulating complex fluid flows, especially for three-dimensional designs. In response, computational models and optimization algorithms have become increasingly sophisticated, and the use of machine learning (ML) has opened up new avenues for reducing costs and improving the accuracy of simulations

3. Machine Learning in Aerodynamics

3.1 Leveraging ML for Aerodynamic Performance Prediction

Machine learning techniques are becoming increasingly important in aerodynamic optimization, enabling faster and more accurate predictions of performance (Fig 3). ML models can analyse large datasets, identify trends, and optimize complex design spaces in ways that traditional methods cannot. This section examines several studies that showcase the transformative potential of ML in aerodynamics.

Fig. 3. This flowchart explains the techniques, applications and benefits of using Machine Learning in Aerodynamics

Study 1: Deep Learning for Aerodynamic Shape Optimization

A study explored the use of deep learning, particularly Generative Adversarial Networks (GANs), to optimize aerodynamic shapes. The study demonstrated that ML could predict aerodynamic performance with fewer simulations, significantly reducing computational costs. The results showed that deep learning models could generate efficient shapes, streamlining the design process.

Study 2: Machine Learning for Drag Reduction

In a study focused on aircraft design, researchers applied ML algorithms to minimize drag while maintaining lift. By using supervised learning, the study identified key design parameters that affect drag, allowing for optimized fuselage and wing shapes that resulted in improved fuel efficiency.

Study 3: Hybrid ML-CFD Approach for Wing Design

Another study combined machine learning with traditional CFD simulations to optimize wing designs for unmanned aerial vehicles (UAVs). By using ML to predict aerodynamic forces, the study accelerated the optimization process, improving design efficiency and reducing the need for time-consuming CFD simulations.

3.2 Advantages and Limitations of ML in Aerodynamics

While ML presents significant advantages in terms of efficiency and predictive accuracy, challenges remain. The complexity of aerodynamic flows, particularly in three-dimensional designs, still poses difficulties for accurate predictions. Additionally, large datasets are required to train ML models, which can be resource-intensive to gather. Nonetheless, research into reinforcement learning and evolutionary algorithms offers promising solutions for overcoming these challenges.

4. Innovations in Materials Science with Machine Learning

4.1 Material Properties and Aerodynamic Design

Materials play a critical role in the performance and efficiency of aerodynamic systems. Lighter, stronger, and more durable materials can enhance the overall performance of vehicles, aircraft, and other structures. In aerospace engineering, materials need to withstand extreme conditions, including high temperatures, mechanical stress, and corrosion. The integration of machine learning into materials science is revolutionizing the discovery and optimization of new materials with tailored properties.

4.2 Predicting and Optimizing Material Properties Using ML

Machine learning is increasingly being used to predict the properties of materials, such as their strength, thermal stability, and fatigue resistance. By analysing large datasets, ML models can identify the relationship between material composition and performance, facilitating the development of advanced materials suited for aerospace applications.

4.3 Materials Science in Aerospace Engineering

Materials science is a cornerstone of aerospace engineering, playing a vital role in enhancing the performance, safety, and efficiency of aerospace vehicles. With the increasing demand for lightweight, durable, and high-performance materials, especially in challenging environments, the integration of machine learning (ML) is transforming material development processes in the aerospace sector.

4.3.1 Lightweight Materials for Fuel Efficiency

One of the primary goals in aerospace design is reducing weight to improve fuel efficiency. Lighter materials lead to reduced energy consumption and improved performance. Carbon fiber-reinforced polymers (CFRPs) and other composite materials have become critical in reducing aircraft weight while maintaining strength and durability. ML is accelerating the discovery of new lightweight materials by analysing large datasets and predicting optimal combinations of properties, which helps engineers design more efficient components 

4.3.2 High-Temperature and High-Strength Materials

Aerospace components, particularly engines and parts exposed to high-velocity airflow, require materials that can withstand extreme temperatures and mechanical stress. Materials like nickel-based superalloys and advanced ceramics are used to ensure the structural integrity of critical components under these harsh conditions. ML models are enhancing the development of such materials by predicting their performance at elevated temperatures and under stress, facilitating the creation of more robust and heat-resistant alloys.

4.3.3 Corrosion Resistance and Durability

Corrosion is a significant issue for aerospace materials, particularly in commercial aviation. ML is being used to predict material behaviours, identifying key factors that contribute to corrosion and degradation. This helps develop materials that are not only resistant to corrosion but also durable under various environmental conditions, improving the overall lifespan and safety of aerospace vehicles.

5. Integrating ML in Aerodynamics and Materials Science

Integrating machine learning (ML) into aerodynamics and materials science is revolutionizing the way engineers optimize designs and predict performance. In aerodynamics, ML models are used to analyse complex fluid dynamics, enabling faster and more accurate predictions of airflow around aircraft and other structures (Fig 4). These models can optimize shapes, reduce drag, and improve fuel efficiency with minimal computational cost. In materials science, ML is enhancing the discovery of new materials by predicting properties such as strength, durability, and thermal conductivity based on their atomic structure. By analysing vast datasets of material behaviours, ML can identify promising candidates for lightweight, high-performance materials. Together, these fields are advancing the development of more efficient and sustainable aerospace technologies, offering faster prototyping, enhanced precision, and deeper insights into material interactions under various environmental conditions.

5.1 Synergies Between Aerodynamic and Materials Optimization

The integration of machine learning in both aerodynamics and materials science creates synergies that can lead to breakthroughs in design and efficiency. By optimizing aerodynamic shapes and materials concurrently, engineers can develop vehicles and structures that are not only aerodynamically efficient but also made from materials that enhance performance and durability. For example, ML can be used to design lightweight materials that complement aerodynamic shapes, resulting in reduced drag and improved fuel efficiency.

6. Conclusion

Machine learning is not only revolutionizing aerodynamic design but also significantly impacting materials science, which is crucial for optimizing the performance of aerospace structures. The integration of ML in material discovery allows for the design of advanced materials with tailored properties, such as improved strength-to-weight ratios and thermal stability, which are vital for efficient aerodynamic performance. Through techniques like inverse design, ML accelerates the development of novel materials that complement aerodynamic shapes, resulting in lighter, stronger, and more sustainable vehicles and structures. As machine learning continues to evolve, it promises even greater advancements in the creation of high-performance materials that meet the increasingly stringent demands of aerospace and automotive industries. Ultimately, the synergy between ML in aerodynamics and materials science offers the potential for groundbreaking innovations in design, leading to more efficient, durable, and environmentally sustainable engineering solutions. 

References

Anderson, J. D. (2016). Fundamentals of aerodynamics (6th ed.). McGraw-Hill Education.

Bodily, P., & Halpin, R. (2019). Application of machine learning for aerodynamics optimization. Journal of Aeronautical Engineering, 5(3), 215-223.

Houghton, E. L., & Carpenter, P. W. (2003). Aerodynamics for engineering students (5th ed.). Butterworth-Heinemann.

Jha, A., Li, Y., & Wang, J. (2018). Machine learning applications in materials science: A review. Materials Science and Engineering: R: Reports, 130, 1-21.

Joubert, D., Salomon, B., & Ruyter, J. (2021). Drag reduction and aerodynamic optimization using machine learning. Journal of Aircraft and Aerospace Technology, 12(2), 45-59.

Sage, P. (2016). Computational fluid dynamics and machine learning: A review. International Journal of Aerospace Engineering, 2016, 1-9.

Ward, L., Paul, A., & Wolverton, C. (2017). Materials informatics: The materials “gene” and big data. MRS Bulletin, 42(8), 645-650.

Xie, L., Zhang, H., & Zhao, X. (2020). Inverse design of materials using machine learning. Materials Today, 31, 1-10.

Zhang, Z., Li, Y., & Wang, R. (2020). Aerodynamic shape optimization using deep learning techniques. Aeronautical Journal, 124(1266), 345-358.

Fig. 1. https://medium.com/how-to-aviation/the-4-forces-of-an-aircraft-c906652aa971

Fig. 4. https://www.sciencedirect.com/science/article/pii/S026412752300518X

About the author

Tejas Maddipatla

Tejas Sai Maddipatla is a grade 11 student at Fiitjee Junior College, Hyderabad. He is studying PCM (physics, chemistry, mathematics) and his favourite subject is Chemistry. Tejas is a tennis player tennis and plays professionally. He is planning to study Material Science in college.

The post Machine Learning in Aerodynamics: Optimizing Designs and Innovating Materials appeared first on Exploratio Journal.

Abstract

In recent years, quantum mechanics has driven the exploration of electronic spin, a fundamental concept in the realm of particle physics. The manipulation of electron spin opens up a realm within optoelectronics, enabling efficient conversion between electronic charge and electromagnetic radiation, based on electronic transitions (the triplet and singlet states). This research seeks to understand and advance organic light-emitting diodes (OLEDs), and their utilization and optimization of the photophysical mechanism of thermally activated delayed fluorescence (TADF). Synthetic strategies for TADF optimization encompass efficient intramolecular spin-crossover mechanisms, including linear polymers and macrocycles. Understanding this multimolecular interplay, encompassing geometries, and excitonics resides in the use of advanced theoretical and computational methods – one that is usually too expensive or complicated to execute. To address these challenges, this research paper will employ a spin-generalized ab initio exciton model (GAIEM) to investigate interunit interactions within macrocycles, with the inclusion of locally excited (LE), charge transfer (CT), and triplet pair (TT) electronic structures underlying spin-singlet and spin-triplet excited states. An important aspect to be explored in this paper is the distinguishability of triplet states by LE and CT character, as well as the energetic proximity between these states. Additionally, the coupling of electronic states will be explored using the two-hole variation of the Tamm and Dancoff Approximation (hh-TDA). This research paper promises to shed light on the complex interplay between quantum spin and optoelectronics, offering valuable insights for the development of future optoelectronic devices and applications.

Introduction

Recently, scientists have been hauled up by quantum mechanics: deciphering the concept of electronic spin. But what is electron spin? Electron spin is the idea that electrons exist as pairs within spatial orbitals through an antiparallel alignment with respect to a magnetic field. But the manipulation of spin opens up a world of possibilities within the realm of optoelectronics – the efficient conversion between electronic charge and the electromagnetic radiation that is based upon electronic transitions. By revealing the mysteries surrounding quantum spin, it could help design the next generation of optoelectronic devices.

Can spin-crossover be controlled at the level of chemical synthesis such that more charge can be channeled toward radiative recombination from electronically excited states? And are there achievable multimolecular assemblies that can be engineered to execute this optoelectronic function through fine-tuning their chemical-bond framework rather than just relying on clumping of individual molecules? These questions lie at the heart of organic light-emitting diodes (OLED) leveraging the photophysical mechanism of thermally activated delayed fluorescence (TADF). TADF requires thermal activation of the spin-crossover from electrically excited triplet to photoexcited singlet states, which results in a delay of radiative recombination following the rate-limiting step of singlet excitation. TADF is grounded in the fourfold multiplication (the amount of substates one can get from a triplet and singlet spin) of emitted photons of light from a 3:1 spin-statistical distribution of triplet:singlet excitations – dramatically improving OLED efficiency (Nobuyasu et al., 2016). Synthetic strategies toward TADF optimization have been discovered from efficient intramolecular mechanisms of spin-crossover to multimolecular methods that enable the fabrication of devices. These methods include linear polymers and macrocycles.

Macrocyclic TADF is fascinating due to the interplay between the units of chemically connected donors (D) and acceptors (A). These donor and acceptor units of excited electron density alternate around a two-dimensional ring. Chemically engineering this macrocycle creates both excitonic effects of delocalization across multiple units of D and A as well as control of the DA architecture (Izumi et al., 2020). Modelling and understanding this multimolecular interplay at the level of geometries and excitonics requires advanced theoretical and computational methods. Theories that stretch beyond the single molecule in resolving excited-state electronic structure are key in the description of excitonic effects when modeling multiunit macrocycles. Moreover, theories that provide finer results of not only the interplay between the individual units but also the electronically excited states they support will become important when modeling the molecular dynamics of the TADF mechanism.

To address this, we will implement a spin-generalized ab initio exciton model (GAIEM) to understand interunit interactions in macrocycles, including locally excited (LE), charge transfer (CT), and triplet pair (TT) electronic structures underlying the spin-singlet and spin-triplet excited states (Li, Parrish and Martínez, 2020). A central question regarding macrocyclic mechanisms of TADF is the extent to which the manifold of triplet states is distinguishable by LE and CT character as well as the energetic proximity between these states. Thus, quantum-chemical calculations will help us unravel these spin manipulating mysteries (Hall et al., 2023). Additionally, the coupling of electronic states through both their configurational character and vibrational activity – the way spin-crossover is thermally activated – will be explored using the hole-hole variation of the Tamm and Dancoff Approximation (hh-TDA) to time-dependent density functional theory (Bannwarth et al., 2020). This is because using hh-TDA offers a more in-depth level of electronic correlation that cannot be reached by particle-hole TDA.

Methods

A. Ground state optimization

In order to analyse molecules at an excited state, we first needed to understand how the molecule behaves at equilibrium – which is also known as the ground state. For the first part of our calculations, we are finding out the ground state nuclear geometry at equilibrium. The ground state is where the molecule sits before it is stimulated by electricity or by light.

To calculate this optimization I chose the functional CAM-B3LYP considering the 6-31G (d,p) basis set. This level of theory is suitable for the calculations because not only does it have a wide range of excited-state natures to analyse, but the functional CAM-B3LYP is known to cure the large destabilisation of the CT states which is observed using other functionals such as B3LYP and PBE0 (Hall et al., 2023). Then with my chosen level of theory, we ran computational quantum calculations to find out the equilibrium geometry of these molecules.

B. Singlet-triplet excited state gap

After calculating the equilibrium geometry of the molecules, we are now able to determine the manifold of electronically excited states; in other words the singlet-triplet gap – which is a representative of how the molecules are excited based on the equilibrium geometry.

To calculate the singlet-triplet energy gap, we used a very similar method to what was used in the article Hall et al., 2023. However, we used a very different system to find our energy gap and used macrocycles – the 2D ring consisting of alternating donor-acceptor molecules – instead (which is more extensive to what Hall et al., 2023 considered in their paper).

C. Triplet excited state optimization

While the calculation of the singlet-triplet energy gap is sufficient enough for us to conclude the manifold of electronically excited states, we decided to take it one step further and analyse the complicated triplet state. Thus, for this last section of calculations, we are trying to optimise the geometry of the triplet state, reflecting what will happen if we electrically stimulate this molecule. In short, we are trying to find out what happens to the triplet after it is excited.

In definition, excitation is the instant in time in which we stimulate an electron to go to the triplet state from the ground state. However, in reality the instant the electron is excited there is no opportunity for the nuclei of that molecule to change positions. Therefore, it is frozen in that instance in time. Thus, we have to subsequently relax the nuclei as the electrons respond to an excitation; and that response is reflected in the triplet excited state geometry optimization.

Results

A. Ground state optimization

First, we need to understand how the molecule behaves at equilibrium. We found out that after 43 cycles, the equilibrium geometric optimization converged – meaning that the nuclear geometry had reached equilibrium. This means that all the values in the 43rd cycle were smaller than the criteria values for ΔE (1.000 x 10-6 J), the gradient RMS (3.000 x 10-4), the gradient maximum (4.500 x 10-4 J), the displacement RMS (1.200 x 10-3 J) and the displacement maximum (1.800 x 10-3 J), respectively.

The further away the value was from the criteria, the weaker the convergence. When there is a difference between iterations of a) the energy, b) the nuclear forces, and c) the nuclear positions, there is a very poor quality of convergence.

Overall, each cycle progressively got closer to the criteria values. As expected, the first 5 cycles of results were well above the criteria level (ΔE: -1.209 x 10-4 J, gradient RMS: 3.434 x 10-4 J, gradient maximum: 9.956 x 10-4 J, displacement RMS: 3.684 x 10-2 J, displacement maximum: 6.754 x 10-2 J after the 5th cycle), indicating that the geometric optimization is still far from the equilibrium. The following 5 cycles gave similar outcomes.

By the 20th cycle the values for ΔE, the gradient RMS, and gradient maximum reached above the criteria values: -7.788 x 10-8 J, 3.809 x 10-5 J, and 9.641 x 10-5 J respectively. The values for both the displacement RMS and displacement maximum had not exceeded the threshold yet. However, between cycles 25 and 30 the value for

ΔE went below the threshold (-4.865 x 10-5 J) and continued to do so followed by the values for gradient maximum (6.818 x 10-4 J) – note that both values were taken by the end of the 30th cycle. Once again, the values for ΔE (3.585 x 10-7 J), gradient RMS (4.878 x 10-5 J), and gradient maximum (1.221 x 10-4 J) exceeded the criteria values by the 37th cycle (17 cycles later). Nearing the last few cycles, the value for displacement RMS finally exceeded the criteria value (by the 39th cycle) with a value of 1.175 x 10-3 J. Finally after 43 cycles all the values exceeded the criteria values, resulting in our final nuclear geometry.

The final values are as follows: -2.275 x 10-7 J (ΔE), 7.164 x 10-6 J (gradient RMS), 2.402 x 10-5 J (gradient maximum), 6.079 x 10-4 J (displacement RMS), and 1.566 x 10-3 J (displacement maximum).

B. Singlet-triplet excited state gap

These next set of results were based on the ground state geometry results we calculated above. But instead of solely finding out the ground state equilibrium, we must calculate the absolute excitation energies of both the triplet and singlet state in order to accurately calculate the singlet-triplet excited state gap.

We found out that at the triplet excited state the energy was 2.61 eV and at the singlet excited state the energy was 3.12 eV. This creates a gap of 0.51 eV based on the ground state optimised geometry.

Based on these results, we concluded that from the ground state to the triplet optimised geometry the gap from the triplet excited state to the singlet ground state has closed; but, the gap from the triplet excited state to the singlet excited state is widening. This is because as the triplet falls further to the ground state – meaning that the triplet moves further away from the higher excited state – the gap between the triplet and the higher excited state increases. Therefore, we can explain the cause of this closing and widening of the singlet-triplet gap through the triplet geometric optimization.

C. Triplet excited state optimization

With this final step of calculations, we are trying to follow the triplet to its equilibrium position in terms of the nuclei. Based on the results from the calculation we can identify that the resulting gap from triplet to singlet actually represents the phosphorescence emission gap. This is when the triplet equilibrium occurs after excitation – allowing the triplet to relax – and from that position it will emit back down to the (singlet) ground state – this is radiation that we call phosphorescence (spin forbidden). We call this the phosphorescence emission energy.

Based on our results, we found out that after triplet geometry optimization, the triplet state lies 2.02 eV above the ground state and the singlet state lies 2.91 eV above the ground state at this geometry. Comparing these results to our previous set of results, we can see that both the triplet and singlet state energy decreased from 2.61 eV to 2.02 eV and 3.12 eV to 2.91 eV, respectively. This is due to the triplet energetically stabilising itself; hence, lowering the energy gap towards the ground state as the triplet relaxes.

Upon observing this, we noticed a widening of the singlet-triplet gap due to the relaxation of the triplet state in equilibrium. The gap we calculated was roughly 0.90 eV. This reflects how the triplet state is being stabilised. However, in the case that the singlet state were to be stabilised geometrically we would see a closing of that gap. But with a triplet state geometrically stabilised there is a widening of that gap because the triplet falls more than the singlet upon stabilisation to its equilibrium geometry – because the triplet falls closer to the ground state.

Do take note that we are comparing the singlet and triplet results based on a fixed geometry and a fixed energy unit, eV, in order to assess the singlet triplet gap – the difference in energy in one geometry – as there is an inability to treat the singlet and triplet character on the same footing. Thus, they must be treated in a balanced way in order for us to trust the results in the energy gap.

Conclusion

Our results provide us with a very nice narrative. It allows us to see the effects on the excited states throughout initial excitation all the way to the final relaxation in the excited state manifold, all which are calculated based on the equilibrium geometry of the molecules.

For these calculations, I selected the functional CAM-B3LYP considering the 6-31G (d,p) basis set because it has a wide range of excited-state natures to analyse and cures the large destabilisation of CT states. Based on this level of theory, we observed that the equilibrium geometry was achieved after 43 cycles (the criteria was reached). Thus, we were able to calculate the absolute excitation energies of both the singlet and triplet states of the molecules during excitation, and from these energy values calculate the singlet-triplet energy gap. Surprisingly, we found that there was a closing and widening of this energy gap through the triplet geometric optimization and sought to calculate the singlet-triplet energy gap the instant we excite it from the ground state to the triplet excited state.

As we were investigating the triplet state, there was a clear closing in the singlet-triplet energy gap due to the relaxation of triplet, at equilibrium, back to ground state. We found out that that was due to the fact that the triplet falls closer to the ground state than the singlet upon stabilisation to its equilibrium geometry; thus, closing the gap between the triplet and singlet ground state and widening the singlet-triplet energy gap.

But while our results correspond with some of the results written in Hall et al., 2023, we must note that our calculations encompassed the TADF macrocycles at only one level of theory. Therefore, in order to investigate the full effects of excited-state geometric optimization we must also consider other levels of theory and the complications in handling the results of triplet character when running quantum calculations.

References

Bannwarth, C. et al. (2020) ‘Hole–hole Tamm–Dancoff-approximated density functional theory: A highly efficient electronic structure method incorporating dynamic and static correlation’, The Journal of Chemical Physics, 153(2), p. 024110. Available at: https://doi.org/10.1063/5.0003985.

Hall, D. et al. (2023) ‘Benchmarking DFT Functionals for Excited-State Calculations of Donor–Acceptor TADF Emitters: Insights on the Key Parameters Determining Reverse Inter-System Crossing’, The Journal of Physical Chemistry A, 127(21), pp. 4743–4757. Available at: https://doi.org/10.1021/acs.jpca.2c08201.

Izumi, S. et al. (2020) ‘Thermally Activated Delayed Fluorescent Donor–Acceptor–Donor–Acceptor π-Conjugated Macrocycle for Organic Light-Emitting Diodes’, Journal of the American Chemical Society, 142(3), pp. 1482–1491. Available at: https://doi.org/10.1021/jacs.9b11578.

Li, X., Parrish, R.M. and Martínez, T.J. (2020) ‘An ab initio exciton model for singlet fission’, The Journal of Chemical Physics, 153(18), p. 184116. Available at: https://doi.org/10.1063/5.0028605.

Nobuyasu, R.S. et al. (2016) ‘Rational Design of TADF Polymers Using a Donor–Acceptor Monomer with Enhanced TADF Efficiency Induced by the Energy Alignment of Charge Transfer and Local Triplet Excited States’, Advanced Optical Materials, 4(4), pp. 597–607. Available at: https://doi.org/10.1002/adom.201500689.

About the author

Xiao Rou Liew

Xiao Rou is currently in Year 13 at the Alice Smith School in Kuala Lumpur. Her academic passion lies within the realm of Organic Chemistry and Evolutionary Biology. Xiao Rou’s dream is to pursue an undergraduate degree in either Materials Chemistry or Evolutionary Biology, with a research focus for both of them.

Aside from her studies, she spends most of my time in the pool. Representing her state – Kuala Lumpur – in numerous national competitions since the age of 10, Xiao Rou aspires to continue to compete against the best in college, hoping to one day represent her country. She also loves to dance, read, and binge watch Hamilton. Nothing gets better than performing to her heart’s content!

The post TADF Macrocycles appeared first on Exploratio Journal.

Abstract

Hepatitis B is a small DNA virus with a diameter of approximately 42nm (Liang, 2009). Globally, around 296 million individuals are infected with the Hepatitis B Virus (HBV), and this infection often leads to the development of cancers and other diseases. Although treatment options exist for HBV, a complete cure is currently unavailable due to the stable nature of its genetic material within host cells, making elimination challenging. Additionally, the virus shares similarities with the human immunodeficiency virus (HIV) and studying these similarities can provide insights into the less understood aspects of HBV infection.

Introduction

Hepatitis B Virus overview and relevance

Hepatitis B infection is caused by the Hepatitis B virus, which is a DNA virus with a partially double-stranded relaxed circular DNA (rcDNA). Its genetic material comprises four open reading frames, which code for seven viral proteins and does not have any non-coding sections. It belongs to the family Hepadnaviridae and possesses an outer envelope composed of lipids and surface antigens known as HBsAg (there are three forms of HBsAg: small S, middle S, and large S). The viral core is composed of core antigens, HBcAg. Another antigen, HBeAg, is closely associated with the viral nucleocapsid and can circulate in the blood since it is a soluble protein. At the 5’ end of the single-stranded section of DNA, there is an attachment to a viral RNA-dependent DNA polymerase (Aryal, 2015).

HBV infection can manifest as either chronic or acute. Only a small percentage, specifically 5%-10%, of adults who contract the virus progress to chronic hepatitis B. However, the risk of developing a chronic infection is significantly higher in individuals who acquire the infection at a younger age, with as many as 90% of infants infected with HBV likely to develop a chronic infection. Approximately 25% of chronic HBV cases advance to liver cancer, making HBV infection the primary cause of liver cancer worldwide (Fast Facts on Global Hepatitis B, 2022). Annually, approximately 820,000 people lose their lives due to HBV infections.

Hepatitis B is transmitted through exposure to blood and bodily fluids, including mother-to- child transmission, and primarily affects hepatocytes. The majority of HBV infections proceed without symptoms (asymptomatic), although an estimated 30% to 50% of infected individuals may experience symptoms such as joint pain, nausea and vomiting, dark urine, pale stool, jaundice, and upper right abdominal pain. Chronic HBV infections can result in liver cirrhosis due to inflammation and increase the risk of developing liver cancers (Aryal, 2015).

Hepatitis B can be diagnosed through a physical examination followed by blood tests, known as a hepatitis panel. These tests specifically check for hepatitis antibodies and antigens (Hepatitis Panel, 2022). A vaccine is available for HBV, and the World Health Organization (WHO) recommends its administration immediately after birth, followed by completion of the 3-dose vaccination series (Hepatitis B, 2022). The current treatment for Hepatitis B virus (HBV) infection focuses on suppressing viral replication and reducing liver inflammation. Antiviral medications, such as nucleoside and nucleotide analogues, are commonly used to achieve these goals. Interferon therapy, which stimulates the immune system, can also be employed. Combination therapy may be considered in certain cases. It is important to note that while treatment can effectively control the virus, it does not eradicate HBV from the body. Long-term or lifelong treatment may be necessary. Regular monitoring and adherence to medication and lifestyle modifications are essential. Individualized treatment plans should be determined in consultation with healthcare professionals.

Hepatitis B poses several challenges for cure. Once inside the cell, it forms a highly stable minichromosome that is difficult to eliminate. Additionally, the virus employs multiple immune evasion strategies to avoid detection by the body, hindering treatment efforts. The progression of the disease is still not fully understood, which makes preventing chronic infection a challenging task. In this context, we delve into the intricacies of these challenges, including the mechanisms of immune evasion, the difficulties associated with eradicating the HBV genome from infected cells, and the similarities between HBV and HIV infections.

Life Cycle of HBV

The entry of HBV into a cell is regulated by two surface receptors: HSPGs (Heparan Sulfate Proteoglycans) and NTCP (Sodium Taurocholate Co-transporting Polypeptide). HbsAg initially binds with low affinity to HSPGs and subsequently with high affinity to NTCP, facilitating the virus’s entry into the cell through receptor-mediated endocytosis. Once inside, the virus undergoes uncoating, and the nucleocapsid is transported to the nucleus via the cell’s microtubule system. The specific timing and location of uncoating remain a mystery.

The compact size of the entire HBV nucleocapsid (~42nm) enables it to enter the nucleus through a nuclear pore complex. Upon nuclear entry, the DNA and capsid separate, and the DNA is converted into covalently closed circular DNA (cccDNA). This cccDNA is highly stable and functions as a “mini chromosome,” being chromatinized, associated with proteins, and subject to transcriptional regulation (Van Damme, Vanhove, Severyn, Verschueren, & Pauwels, 2021). Even in patients who have fully recovered from HBV infection, detectable levels of cccDNA persist and can be reactivated under immunosuppression, highlighting its stability (Hong, Kim, & Guo, 2017).

In addition to encoding viral proteins, the cccDNA also serves as a template for the virus’s pregenomic RNA (pgRNA), which associates with the viral DNA polymerase and is encapsidated to form an immature virion. The pgRNA is subsequently reverse transcribed and matures into rcDNA. Mature virions can either exit the cell through exocytosis to infect other cells or re-enter the nucleus to contribute to the cccDNA pool. Immature virions are highly phosphorylated, preventing their exit from the cell. As they mature, the phosphate groups are removed, allowing the mature virions to move to the Golgi body for exocytosis (Schädler & Hildt, 2009).

Formation and regulation of HBV cccDNA

After entering the nucleus, the HBV rcDNA is separated from the viral polymerase. The partially double-stranded DNA undergoes completion, and its ends are covalently ligated by host DNA ligases, leading to the formation of covalently closed circular DNA (cccDNA) through a damage response pathway. The cccDNA becomes associated with both histone and non-histone proteins, resembling human chromatin (Xia & Guo, 2020), and it is transcribed by host RNA polymerase-II. The transcriptional state of cccDNA is regulated by modulating the acetylation states of the histones bound to cccDNA.

cccDNA contains three CpG islands (CGIs) where DNA methylation can occur. Methylation at CGI-1 is rare, while methylation at CGI-2 is associated with low viral loads due to reduced pgRNA synthesis and lower cccDNA replication. Methylation at CGI-3 is associated with the development of hepatocarcinoma (Hong, Kim, & Guo, 2017).

The transcriptional activity of HBV cccDNA is modulated by four promoters and two enhancers. Enhancer I controls the activation of HBx protein transcription, while enhancer II controls the transcription of all other genes (Xia & Guo, 2020). The viral HBx protein serves as a transactivator, promoting the transcription and expression of HBV proteins. The absence of HBx is associated with histone deacetylation and the accumulation of repressive markers in cccDNA (Hong, Kim, & Guo, 2017).

Immune evasion by HBV

Due to the persistent and highly stable nature of cccDNA, complete eradication of HBV from infected cells is challenging. Therefore, an effective approach to combat HBV infection is to eliminate the infected cells, which is accomplished through the immune system’s response upon viral detection.

However, HBV has developed strategies to evade detection by the host immune system. Upon HBV infection, the downregulation of class-I MHC molecules leads to the activation of natural killer (NK) cells. In chronic infection, NK cells remain chronically active and contribute to liver fibrosis. Kupffer cells (KCs) increase the expression of class-II MHC molecules to prime CD4+ and CD8+ T cells, but during chronic HBV infection, KCs exhibit abnormal function, possibly due to the downregulation of TLR-2. Additionally, during chronic HBV infection, dendritic cells (DCs) have impaired production of IFN-α.

To evade antibody detection, HBcAg and HBsAg are secreted in large amounts, effectively masking other antigens through extensive antibody binding. HBsAg has been found to downregulate TLR-3 signalling. STAT-1 and STAT-2 play a role in downregulating cccDNA activity and are critical in the interferon-induced JAK/STAT (Janus Activated Kinase/Signal Transduction and Activator of Transcription) pathway. The HBV polymerase prevents the entry of STAT-1/STAT-2 heterodimers into the nucleus, effectively inhibiting the interferon response. HBeAg inhibits TLR-2 expression in KCs, impairing their response. Additionally, various HBV proteins directly interact with and inhibit host cell immune activation. During chronic infection, HBV can selectively lose its HBeAg to conceal itself from immune detection (Ortega-Prieto & Dorner, 2017).

Similarity to HIV

HIV (Human Immunodeficiency Virus) is a retrovirus that primarily targets CD4+ T-cells. It encodes a reverse transcriptase and, similar to other retroviruses, integrates into the host cell genome with the assistance of integrase. HBV possesses an RNA-dependent DNA polymerase, functioning as a reverse transcriptase, enabling replication. Both viruses exhibit high mutation rates during reverse transcription due to the absence of exonuclease proofreading, resulting in hypermutability (Roberts, Bebenek, & Kunkel, 1988). Consequently, HIV and HBV exist as quasispecies, characterized by significant genetic heterogeneity (Nowak, 1992).

Due to the persistence of viral genetic material within the host cell nucleus, complete eradication of HIV or HBV has proven challenging. Current treatments focus on reducing the viral load to undetectable levels. Although most HBV patients achieve complete recovery with undetectable HBV DNA levels, HIV rarely leads to recovery. However, in cases of chronic HBV infection, both HIV and HBV can remain dormant for years before reactivation (Sharma, Saini, & Chawla, 2005).

Individuals with HIV have a higher susceptibility to various cancers, including “AIDS-defining cancers” like Kaposi’s Sarcoma, due to their compromised immune system (Hernández- Ramírez, Shiels, Dubrow, & Engels, 2017). Similarly, individuals with HBV are more prone to gastrointestinal and liver cancers due to genomic instability caused by cccDNA and the activity of HBx protein (Massimo & Zucman-Rossi, 2016). Upon HIV infection, immune evasion prevents the activation of interferon (IFN) responses (Guha & Ayyavoo, 2013). Similarly, in HBV infection, the JAK/STAT pathway is blocked, impairing the IFN response.

Conclusion

Every year, hepatitis B claims the lives of 820,000 individuals. Gaining a comprehensive understanding of the disease mechanisms and the challenges associated with finding a cure is crucial. Currently, it is known that the stability of HBV cccDNA, resembling a human chromosome, hinders its complete removal from infected cells. Furthermore, HBV employs a diverse array of immune evasion strategies, such as the overproduction of HBcAg and HBsAg, which enable it to persist undetected in the body for extended periods. Its resemblance to the deadly Human Immunodeficiency Virus suggests that insights from one may aid in finding a cure for the other.

References

Aryal, S. (2015). Hepatitis B Virus- Structure, Epidemiology, Symptoms, Pathogenesis, Diagnosis, Treatment and Vaccines. Retrieved from MIcrobiology info: https://microbiologyinfo.com/hepatitis-b-virus-structure-epidemiology-symptoms- pathogenesis-diagnosis-treatment-and-vaccines/

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About the author

Tara Kumar Bailkeri

Tara is a 12th Grade student at Primus Public School in Bangalore. Her passion lies in Biology, Chemistry, and Physics, and she dreams of pursuing undergraduate studies in Biomedical Sciences with a research focus. She takes a keen interest in molecular and cellular biology and is fascinated by how simple interactions can create complex biological systems.

Tara is more than just a studious student. She has a well-rounded personality honed by years of professional training in “Bharatanatyam,” a traditional Indian dance form. She loves dancing, painting, playing the Mridangam (an ancient Indian rhythm instrument), and strumming the guitar. In addition, she has participated in Model UN forums and is an adrenaline junkie who loves adventure sports. She is also an avid reader and writes in her spare time.

The post The difficulty in curing Hepatitis B and how it is similar to HIV appeared first on Exploratio Journal.

Abstract

Mycorrhizae are symbiotic relationships between plants and fungi in which the fungi supply the plant with water, nutrients, and other beneficial substances to maximize their growth, and receive carbohydrates and organic compounds by the plant’s ability to photosynthesize. Arbuscular mycorrhizal fungi (AMF) are the most abundant type of mycorrhizae and is the most well-researched. Agriculture is harmful for the climate and the environment, and projections in human population will only increase the pressures of agriculture. The topic of mycorrhizae has suddenly attracted the interest of many scientists and intense studies because of its potential benefits in croplands, which could reduce fertilizer use (reducing greenhouse gas emissions) and increase crop yields (increasing food security). However, because the subject has recently been brought about, not enough research has been conducted to confirm the mechanics, benefits, and potential utility of AMF in crop plants. This study offers a detailed and unified view of two of the most important benefits of mycorrhizae in various crops: its ability to increase water and nutrient uptake and to enhance resistance to abiotic stress.

Introduction

Mycorrhizae is a symbiotic association between plants and fungi. Plants produce organic compounds, such as carbohydrates, by photosynthesis and supply them to fungi through root tissues. In return, fungi supply water and nutrients to plants through their high absorptive ability because of the large surface area of fungal hyphae (Selosse et al. 2006). In some areas, such as lowland forests, fungi can create a network between all trees and use them to exchange nutrients (Read and Perez-Moreno 2003). There are four types of mycorrhizae: arbuscular mycorrhiza, orchid mycorrhiza, ectomycorrhiza, and ericoid mycorrhiza. Arbuscular mycorrhizal fungi (AMF) are the most common fungi in soils, representing up to 55% of the soil microbial biomass and forming a symbiosis with more than 80% of vascular-cultivated plants (Diagne et al. 2020). AMF are natural root symbionts that provide essential inorganic nutrients to host plants, thereby improving growth and yield under various conditions (Begum et al. 2019). As a biofertilizer, AMF can potentially benefit plants in many ways, thereby playing a vital role in agricultural ecosystems.

It is becoming increasingly crucial to supply food to a growing human population while mitigating the effects of climate change. The world’s population is more than three times larger than that of the mid-twentieth century. The global human population has reached 8 billion by 2022, 5.5 billion more than in 1950. The world’s population is expected to increase by nearly 2 billion people in the next 30 years, from the current 8 billion to 9.7 billion in 2050 and could peak at nearly 10.4 billion in the mid-2080s (Roser and Rodés-Guirao, 2013). A greater population leads to greater demand for food, water, and natural resources. Agriculture accounts for 10-15% of greenhouse gas emissions (Houser and Stuart 2020). Nitrogen fertilizers, popular chemical fertilizers used to grow crops today, emit large amounts of greenhouse gases. This occurs when the fertilizer runs off into waterways or is broken down by soil, thereby releasing nitrous oxide gas. Population growth and greenhouse gas emissions are among the reasons why the study of mycorrhizae is important.

Because mycorrhizae are a relatively new subject, little research has been conducted on this topic, and agricultural and environmental ramifications are unclear. Field studies have shown that AMF increase nitrogen uptake in wheat crops by 12.9% (Giambalvo et al. 2022) and onion crops by 75% (Mollavali et al. 2018). It is unclear why results differ greatly between different crop plants, and the fact that experiments and studies only target one type of crop, or one aspect of mycorrhizae does not help. There is much ambiguity surrounding the fascinating idea of using mycorrhizae to increase crop yields, which should not be the case, since there are many clear benefits. Much research is needed to identify how impactful mycorrhizae can be in the field of agriculture and in the face of climate change. In this paper, I discuss the benefits of mycorrhizae in increasing water and nutrient uptake and plant tolerance to abiotic stress.

1. Mycorrhizal benefits in water and nutrient uptake

Mycorrhizae increase water uptake through their wide network of filaments, which gives the plant a larger soil contact surface area than they can reach alone (Huey et al. 2020). Collectively, the extremely fine threads of mycorrhizal hyphae, a long and branching filament structure of a fungus, act as a sponge to absorb water when it becomes available and holds onto it for times of need (“Water Acquisition” n.d.). In other words, they act as water reservoirs for plants and increase their ability to absorb water. This factor will emerge later as we discuss the benefits of mycorrhizae in increasing the tolerance of plants to environmental stress. The absorptive area of mycorrhizal hyphae is approximately 10 times more efficient than that of root hairs, with the longest area reaching 10 cm (Huey et al. 2020). In an experiment performed by J. M. Ruiz-Lozano and R. Azcón, the two growing heads of lettuce, were placed in separate compartments divided by a mesh. One compartment contained only the roots. The other specimen had AM hyphae. Water was accessible to both plants because of the steel mesh. The results indicated that more water was taken up by the compartment with AM (Ruiz-Lozano and Azcón 1995), indicating their superior absorptive abilities and water uptake.

Mycorrhizae increase phosphorus uptake. Because phosphorus dissolves in water, mycorrhizae indirectly take up phosphorus through hyphae when taking in water for the plant. However, inorganic phosphorus is scarce in the soil because it has a low diffusion rate, is sequestered by minerals like iron and aluminum hydroxides, and is bound to organic matter (Lambers, Martinoia, and Renton 2015). The availability of phosphorus is further compromised by geological aging and weathering in the soil (Clausing and Polle 2020). Plants adjust to varying phosphorus availability by adopting root systems that explore organic soil more intensely in phosphorus-poor soil than in phosphorus-rich soil (Lang et al. 2016). Mycorrhizae send signals to plants, such as LjPT4 and MtPt4, which induce a genetic process by which the plant is more effective in absorbing phosphorus (Posta et al. 2019). Some studies have shown that AMF can contribute up to 90% of the plant phosphorus uptake (van der Heijden et al. 2015).

Mycorrhizae can also increase nitrogen uptake. Nitrogen can be present in either organic or inorganic forms in soil. Plants require inorganic nitrogen because, among other vital processes, it is essential for photosynthesis. The main role of mycorrhizae is to access more inorganic nutrients, such as nitrogen. The mycorrhizal sheaths of the trees contributed 50% or more of the total nitrogen input into the Douglas fir ecosystem (Read and Perez-Moreno 2003). Similarly, Swedish coniferous forest soils contained up to 20% of the total nitrogen in a given horizon that could be located in the fungal mycelium (Read and Perez-Moreno 2003). The evolution of saprotrophic ancestors, fungi that obtain nutrients by feeding on organic matter, plays a major role in the ability of fungi to obtain inorganic nitrogen (Johnson and Gehring, 2007). By adapting their metabolism to the availability of nutrients in the soil, fungi are able to produce a mixture of oxidative and hydrolytic enzymes to break down lignocelluloses, such as wood (“Fungal Extracellular Enzyme Activity” 2023). The fungi release the enzymes into the surrounding soil, which consume organic matter, such as decomposing wood, and turn it into inorganic nitrogen in a process called nitrogen mineralization. This process is crucial to nutrient uptake in plants, as it cannot trigger this chemical reaction by itself. The fungi then trade inorganic fungi to the host plant to receive benefits, such as carbon and carbohydrates.

Additionally, mycorrhizae can reduce nutrient losses, a problem commonly overlooked in discussions of the ways mycorrhizae can bring in nitrogen, rather than how it could prevent it from being removed. Nutrient loss, especially in nutrient-poor ecosystems, reduces plant productivity and the overall integrity of the crops produced. Nutrient loss is primarily a result of leaching and loss of water-soluble plant nutrients due to rainfall and irrigation. This creates losses of up to 50-60% of all applied nitrogen (Melissa 2018). Grassland microcosms with AM fungi lost 60% less phosphorus and 7.5% less ammonium compared to control microcosms without AM fungi (van der Heijden 2010). Similar results were obtained for microcosms planted with three different grass species (van der Heijden 2010). Mycorrhizae prevent leaching in two ways: their widespread and complex root system and their enhanced nitrogen interception. As with water, increasing the absorptive surface of the root system allows AMF to explore a larger soil volume and access more nutrients (Asghari and Cavagnaro 2012). Studies have also shown that the regulation of key genes involved in nitrogen transport and assimilation indicates a shift towards nitrogen uptake via the mycorrhizal pathway in the mycorrhizal genotype (Asghari and Cavagnaro 2012). Further work is needed to test the effects of mycorrhizae on nutrient loss. Unfortunately, few studies have been performed under field conditions because of experimental constraints. Many experiments performed in controlled environments may yield biased and incomplete results. The combination of some of the studies that have been performed show that AMF increases nitrogen uptake by 54.3% and phosphorus uptake by 55-67-59.67%.

2. Mycorrhizal benefits in increasing plant tolerance to abiotic stress

Abiotic stress is the negative effect of non-living factors on living organisms, which hampers plant growth and productivity. These include drought, salinity, low or high temperatures, metals, and other environmental extremes (Stanca et al. 2003). Abiotic stress is further increased by climate change and agricultural malpractices such as the excessive use of nitrogen fertilizers and artificial pesticides. It is important to note that abiotic stress is much more genetically complex and more difficult to control than biotic stress, the condition in which plants cannot sustain their normal growth due to certain interferences from deleterious microorganisms, such as bacteria and viruses (Yahia 2019). Therefore, there is an increasing need for bio-fertilizers such as AMF, which are believed to provide tolerance to abiotic stress and prevent the decrease in key metabolic pathways in crop plants (Begum et al. 2019).

Water scarcity is the number one abiotic stress for crops in all soils, including arid, semi-arid, and even agricultural areas where crops are grown. Droughts have been further enhanced by climate change. A warmer temperature reduces surface water and dries out soil and vegetation. Droughts reduce plant nutrient uptake by decreasing both nutrients created by mineralization and nutrient diffusion throughout the soil (Bárzana and Carvajal 2020). Lack of nutrients negatively affects plant growth, crop yield, and quality. Over the past 35 years, worldwide droughts have decreased maize yields by 40% and wheat production by 21% (Posta et al. 2019). This is where mycorrhizae come in, or specifically, AMF. As discussed above, the hyphal network of AMF reaches a larger area of soil and can transport water through its fine filaments to the plant. Although this helps to provide the plant with water during droughts, another important factor is that AMF can improve the water-holding capacity of the soil. AMF produces glomalin or glomalin-related soil protein (GRSP), which binds to individual soil particles. GRSP enhances water retention in soils by protecting C-rich debris from decomposition by soil microbes (Posta et al. 2019). AMF hyphae interact with GRSP and physically stick microaggregates with macro-aggregates, which form stable aggregates under water-scarce conditions (Nichols 2008). In artificial substrates, host plants are less stressed in soil inoculated with AMF under drought conditions (Posta et al. 2019), which suggests that AMF increases a plant’s tolerance to water-scarcity stress by enhancing water retention in the surrounding soil.

Salinity, an increase in soil salt concentration, is another type of abiotic stress that is extremely degrading to crop plants. This problem is intensified by manmade irrigation systems in arid and semi-arid lands with low rainfall. Inadequate irrigation systems create salinity, which negatively affects 20% of all irrigated land (Glick et al. 2007). Salts in the soil occur as ions released by decomposing minerals in the soil. They can also be applied by irrigation and groundwater. When precipitation is insufficient to wash away these ions from the soil, salts begin to accumulate (Shrivastava and Kumar 2015). Although plants are able to absorb essential nutrients in the form of soluble salts, an excess of it simply overbears the root system, which leads to suppressed growth and other problems. Sodium is non-essential in most plants. However, excess potassium causes an ionic imbalance by interfering with potassium functions, which can lead to nutrient deficiency and the death of plant cells (Assaha et al. 2017). AMF can mitigate the effects of salt stress by maintaining a balance between sodium and potassium levels, which is key to survival in saline environments. AMF naturally form symbiotic relationships with plant species in saline environments (Juniper and Abbott 2006) and can improve the ionic balance between K and Na through selective uptake.

Another type of abiotic stress is caused by heavy metals, including Pb, Ni, Cu, and Hg. Heavy metals are toxic to plants and have been shown to suppress shoot and root growth, leaf chlorosis, and even death in soils enriched with cadmium and zinc (Moghadam 2016). Heavy metal contamination is one of the most serious hazards to agricultural ecosystems and human health because it is extremely common and non-biodegradable (Riaz et al. 2021). AMF mitigate the effects of heavy metal contamination by assisting phytoremediation and the use of microbes to reduce the concentrations of toxic substances in the environment (Yan et al. 2020). AMF-assisted phytoremediation is gradually gaining attention because it is an inexpensive and environmentally friendly technique with fewer secondary pollutant effects (Morar et al., 2018). Mycorrhizae can directly and indirectly mitigate the stress of heavy metals. Immobilizing heavy metals directly in the hyphae of mycorrhizal fungi can fix them in the cell wall and store them in the vacuole (Begum et al. 2019), thereby reducing metal toxicity in plants. In an experiment performed on alfalfa, AMF improved the tolerance of plants to Cd because of the immobilization of heavy metals (Wang, Huang, and Gao 2012). AMF can indirectly induce changes in host plants to cope with toxic environments by improving water and nutrient uptake (Riaz et al. 2021). As observed in Lolium perenne, or perennial ryegrass, AMF settle polluted soils and reduce the uptake and accumulation of heavy metals including cadmium, nickel, and zinc (Takács and Vörös 2005). In an experiment performed on rice, AMF was extremely effective in lowering the levels of Cd in both vacuoles and cell walls (Li et al. 2016). The many benefits of mycorrhizae decrease abiotic stress and allow crops to grow and prosper under extremely harsh conditions.

Conclusion

This study focused on the mycorrhizal benefits of increased water and nutrient uptake and increased resistance to different types of abiotic stress, including drought, salinity, and heavy metal toxicity. Again, because mycorrhizae are still a relatively new topic in the scientific field and have not been thoroughly researched, the agricultural and environmental consequences remain unclear. However, it is safe to say that it is definitely in the running for the key to pressing environmental issues. A compilation of the few experiments that have been performed show that AMF increases nitrogen uptake by 54.3% and phosphorus uptake by 55.67-59.67%, which is a huge indicator of its true potential. AMF is also proven to improve plant tolerance to droughts, salinity, and heavy metals toxicity. Perhaps gaining a wider and more complete view of the benefits of mycorrhizae will raise new questions that reach the heart of the problems at hand, such as how exactly we can apply the benefits of mycorrhizae to mitigate the effects of challenges we face today.

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About the author

Madalyn Shen

Madalyn is a 10th grader at the Germantown Friends School. She loves learning about sustainable methods that people can apply to their everyday lives, especially in light of serious environmental problems. She has an aquaponics system in her backyard, which is a great way of growing organic vegetables and keeping her fish and turtle tank clean. Madalyn also plays tennis to alleviate stress.

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Abstract

 Stem cells have been a prevalent research field for their versatile use as a potential treatment of several different diseases that have remained untreatable, such as Alzheimer’s disease. These cells are unspecialized cells that can replicate themselves indefinitely (self-renewal) and give rise to more specialized cells (differentiation). Due to these unique properties, stem cells can be described as the foundation for all tissues and organs in the human body. Stem cells can be mainly classified into 3 categories: embryonic stem cells (ESCs), induced-pluripotent stem cells (iPSCs) and adult stem cells. The different distinctive characteristics of each category of stem cell as well as their applications in the potential treatment development of Alzheimer’s Disease are discussed in this manuscript.

1. Introduction

In the last decade, stem cells have raised great interest in the scientific community due to their promising use in the fields of regenerative medicine, drug testing and discovery and modeling of healthy and diseased tissues. Stem cells are undifferentiated cells that can be isolated from embryos (embryonic stem cells, ESCs), from adult tissues (adult stem cells) or can be generated by reprogramming somatic cells, such as dermal fibroblasts (induced pluripotent stem cells, iPSCs) (Zakrzewski et al, 2019, Shi et al, 2017).  The three different types of stem cells each have potential advantages and disadvantages that may encourage or limit their use for medical applications. Embryonic stem cells are pluripotent, meaning that they can differentiate or specialize in any cells of the body, with the exception of the extraembryonic cells, which constitutes the placenta and the umbilical cord (Zakrzewski et al, 2019).  While pluripotency and plasticity makes ESCs the most desirable cell type for regenerating diseased tissues, ESCs raise the ethical issue of using them since their isolation from the inner cell mass destroys the embryo (Abdulrazzak et al, 2010). Additional to their ethical concerns, because of their pluripotency and proliferative ability, they can form tumors after transplantation (Hess et al, 2019). Because of their limited proliferative capacity, adult stem cells do not raise this issue, however they are much harder to culture in vitro and are considered multipotent or unipotent, in that they can only differentiate into fewer or just one specific cell type. Induced pluripotent stem cells are a rather new discovery so not much is known about them on their use for potential treatments, however they are the most promising because of their versatility and easy access (Shi et al, 2017). Similar to ESCs, iPSCs have the potential issue of tumor formation due to their proliferative ability. 

2.1. Embryonic stem cells (ESCs) 

Embryonic stem cells are stem cells that are found in the inner cell mass of a blastocyst. A blastocyst is a bundle of cells formed 5-6 days after fertilization of the oocyte by the sperm cell that undergoes cell division by meiosis (Yu and Thompson, 2006). ESCs have been differentiated in multiple  cells types such as cardiac cells (Liu et al, 2018) , vascular smooth muscle cells (Cheung et al, 2011) , nerve cells (Magown et al, 2017, Jones et al, 2018) and retina cells (Lakowski J, 2015,Mehat et al, 2018) to form healthy tissues that can be transplanted in injured or diseased areas of the body. ESCs can be easily identified and isolated from the inner cells of the blastocyst (Sills et al, 2005) and can be cultured and grown indefinitely in a lab setting to be used for research in medicine and science. These extracted stem cells are extremely valuable and have shown great potential for regenerative treatments as well as for drug development and testing (Yu and Thompson, 2006).

2.2. Induced pluripotent stem cells (iPSCs)

Induced pluripotent stem cells are derived from somatic cells such as skin or blood cells and are reprogrammed into an embryonic-like state (Shi et al, 2016). Researchers associated pluripotency, unique to ESCs, with genes or factors that are only expressed by ESCs. In 2006, Shinya Yamanaka, identified four genes (Myc, Oct3/4, Sox2 and Klf4) with encoded transcription factors that could convert somatic cells into pluripotent cells (Zhao et al., 2013). The reprogramming of somatic cells with the introduction of these four genes led to the discovery and use of iPSCs which have paved a way to more efficiently identify and model disease cells that were not very successful in animal models. After their discovery, iPSCs have provided a strong headway into regenerative medicine, to repair damaged cells, tissues or organs. When conducting a normal tissue or organ transplant, it is imperative that the cell, tissue or organs donor’s physiological profile matches that of the patient. Not being able to meet these specific conditions is one of the common reasons patients die in urgent situations of accidents or patients who have been suffering degenerative diseases. The use of iPSCs, that can be reprogrammed from the healthy cells of the same patient, can greatly reduce the risks that come with transplants. Since iPSCs can be directly generated from skin and blood cells of patients, the cells which will be transplanted are from the patient’s own body. In addition, patient-specific iPSCs allow researchers to look more closely at the disease relevant cells in the patient’s body.

2.3. Adult stem cells 

In contrast to embryonic stem cells, adult stem cells or somatic stem cells are stem cells found in the adult body, especially in the bone marrow, blood vessels and in the adipose tissue (Stem cells, 2001). They are found in the adult tissues and are more difficult to expand in a lab setting since they do not duplicate as easily as ESCs and iPSCs (Stem cells, 2001), therefore they can be cultured for a lower number of passages in vitro, yielding smaller numbers of cells. These cells are more specialized compared to ESCs and iSCs and usually only give rise to limited types of cells, dependent upon the tissue type they have been isolated from (Stem cells, 2001). For example, stem cells found in the bone marrow differentiate into red blood cells, white blood cells, and platelets, however, they will not differentiate into cells of other tissues, such as liver, or brain cells. In a few cases, it has been demonstrated that adult stem cells of various tissues could be reprogrammed into IPSCs (Labusca et al, 2019) 

3. Use of stem cells for treating Alzheimer’s disease (AD)

Alzheimer’s, a disease characterized by memory loss and cognitive impairment has troubled society due to its unknown causes as well as lack of treatment for the disease. With the transplantation of stem cells, researchers can investigate how these regenerative cells could potentially slow down the development of AD in the brain as well as using them as new drug screening platforms by differentiating them into multiple brain cell subtypes.

3.1 In vitro models of AD 

The use of stem cells for the development of in vitro platforms for drug screening and discovery allow to create patient-specific models of diseases, including AD. With these models, it is possible to examine the effects of promising drugs on the cell types most relevant to AD and to screen through a variety of compounds that directly target parts of the brain in relation to the disease itself.

In this context, stem cells are used as a source to differentiate healthy cells of the brain for studying molecular pathways and physiologic/pathologic cell phenotypes in vitro. In particular, ESCs and iPSCs have been differentiated into different cells of the brain, including distinct neuronal and glial cell subtypes, as well as astrocytes (Little et al, 2019).

Furthermore, cells from patients carrying specific mutations associated to AD have been isolated and reprogrammed to iPSCs and further differentiated into neurons, to determine the optimal, patient-specific treatment to attenuate the effects of the pathology (Yagi et al, 2011, Israel et al, 2012, Muratore et al, 2014).

3.2 Stem cell therapy for AD

In addition to drug screening applications, stem cells can be directly used as therapeutic agents to slow down the progression of the disease. 

Arnhold et al. demonstrated for the first time the possibility of differentiating ESC-derived neural precursor cells into neurons and astrocytes directly after transplantation in adult rat brain (Arnhold et al., 2000). The authors also demonstrated that the precursor cells were mature and fully functional after transplantation. Several studies showed that after transplantation, ESCs are able to integrate with other cell types in the brain (Nasonkin et al., 2009) and secrete reparative molecules to regenerate injured regions (Zhang et al., 2006). Furthermore, it has been demonstrated that the ESC-derived neuron precursors are able to integrate with the host brain tissue. Aubry et al (year) suggested that the optimal number of cells to be implanted has been found to be 15×103 cells per animal.

iPSCs therapy is relatively new for Alzheimer’s disease. There are several studies in literature focused on the use of iPSCs for developing 2D and 3D models of brain diseases, including Alzheimer’s disease. Since iPSCs can be derived from AD patients, researchers have the ability to look at the specific genes and molecular pathways  associated with the disease in the neural cells (Shi et al., 2017). Furthermore, since these cells self-renew themselves, researchers can recapitulate how the disease grows and progresses and behaves in simple in vitro disease models (Shi et al., 2017).

Furthermore, it has been recently demonstrated that neurons can be successfully differentiated from fibroblasts (skin cells) reprogrammed into iPSCs (Liu et al, 2012). These cells have demonstrated similar morphology and function as these of the neural cells in the brain. Transplanted iPSCs-derived neurons are able to survive, maintain their function and even mature into the brain of mice models (Eckert et al 2015, Fujiwara et al, 2013). Promising therapeutic effects have been shown by Eckert et al, that demonstrated attenuated post-stroke effects and augmented neurological function in a mice model of stroke starting only 24 hours after the injection of iPSC-derived neurons (Eckert et al, 2015).

Fewer research studies are available that use adult stem cells for brain regeneration and AD. Stem cell therapy with adult stem cells often involves the use of mesenchymal stem cells (MSCs) and their expansion and  differentiation into neural cells (Liu et al., 2020) however these cells exhibited a low yield of differentiation and decreased stability compared to ESCs- and iPSCs-derived neurons when transplanted in vivo.

4. Conclusion

This paper describes just a few out of many examples of how stem cells can be used for the cure and research of AD. There are countless diseases in the world today with no known cure, but extensive research of stem cells could bring about countless possibilities and opportunities in which they can be used to cure untreatable diseases through applications in regenerative disease as well as disease modeling. Stem cell ability to differentiate into any cell type  and to recapitulate patient-specific diseases will allow for more ground-breaking methods and treatments to be discovered by studying more thoroughly at how the disease behaves. With more research on the use of stem cells for Alzheimer’s Disease, researchers will be able to look at the development of AD in neural cells and hopefully show promising results for a treatment that no one will fail to remember.

Stem cells which could be described as the most basic form of life were discovered about 30 years ago. These small cells which are in their simplest forms not only give rise to the most complex structures in the body but also show a hopeful future and solution for devastating diseases in the world today. It’s a gift in the simplest of essences yet in the most intricate of ways.

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About the author

Dana Chung

Dana is a student at the Coventry Christian School in Pennsylvania.

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